{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/wenbinyao/anaconda3/lib/python3.7/site-packages/libpysal/examples/remotes.py:26: UserWarning: Remote data sets not available. Check connection.\n",
      "  warnings.warn(\"Remote data sets not available. Check connection.\")\n",
      "/home/wenbinyao/anaconda3/lib/python3.7/site-packages/pysal/__init__.py:65: VisibleDeprecationWarning: PySAL's API will be changed on 2018-12-31. The last release made with this API is version 1.14.4. A preview of the next API version is provided in the `pysal` 2.0 prelease candidate. The API changes and a guide on how to change imports is provided at https://migrating.pysal.org\n",
      "  ), VisibleDeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pymysql\n",
    "import copy\n",
    "import seaborn as sns\n",
    "import fiona\n",
    "import matplotlib as mpl\n",
    "import geopandas as gpd\n",
    "from shapely.geometry import Point,Polygon,shape\n",
    "from coord_convert.transform import wgs2gcj, wgs2bd, gcj2wgs, gcj2bd, bd2wgs, bd2gcj \n",
    "import math\n",
    "import imp\n",
    "import plot_map\n",
    "imp.reload(plot_map)\n",
    "import esda\n",
    "from geopandas import GeoDataFrame\n",
    "import libpysal as lps\n",
    "from shapely.geometry import Point\n",
    "import pysal\n",
    "%matplotlib inline\n",
    "import time\n",
    "from scipy.stats import chi2\n",
    "from matplotlib import patches\n",
    "import gc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据库连接成功.\n"
     ]
    }
   ],
   "source": [
    "# 连接mysql数据库\n",
    "try:\n",
    "    conn = pymysql.connect(host='localhost', user='', password='', db='', charset='utf8')\n",
    "    print('数据库连接成功.')\n",
    "except:\n",
    "    print('数据库连接失败.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1.第一阶段获得每辆车的出行链\n",
    "\n",
    "此处出行链指车辆一天被检测器检测的的点位按照时间顺序排序。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def toSeq(ser):\n",
    "    # 将每一辆车每一次被检测的信息构成一个list，并存放到dataframe的一个字段里。\n",
    "    return [ser[2],ser[3],ser[4]]\n",
    "\n",
    "def merseq(df):\n",
    "    # 将一辆车所有的被检测信息均合并到一个list中，该list是一个二维数组，每一个元素是一个list，记录该车辆的时空出行链。\n",
    "#     print(df)\n",
    "#     print(\"----------------------------------------------------------------------------------------\")\n",
    "    return list(df['seq'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# 统计第一阶段各天每辆车每次trip的OD的时空信息\n",
    "conn.ping(reconnect=True)\n",
    "cur = conn.cursor()\n",
    "tablenames = ['20191217','20191218','20191220','20200106','20200110','20191223','20191224','20191225','20191226','20191227']\n",
    "FirStaOD = dict()  # 用一个字典来存储第一阶段各天的每辆车每次trip的OD的时空信息\n",
    "for table in tablenames:\n",
    "    sql = '''\n",
    "    select carnum1 , carnum2 , time , LNG , LAT from {}aj2 where (type = 2 or type = 52) and (lng is not null and lat is not null and \n",
    "    lng > 119 and lng < 121 and lat > 29 and lat < 30)\n",
    "    '''.format(table)\n",
    "    cur.execute(sql)\n",
    "    conn.commit()\n",
    "    tmptable = cur.fetchall()\n",
    "    tmptable = pd.DataFrame(tmptable)\n",
    "    tmptable.rename(columns = {0:'carnum1' , 1:'carnum2' , 2:'time' , 3:'LNG' , 4:'LAT'} , inplace = True)\n",
    "    tmptable['carnum'] = tmptable['carnum1'] + tmptable['carnum2']\n",
    "    # 得到车辆每一天的出行链\n",
    "    tmptable.sort_values(by = ['carnum' , 'time'] , ascending = (True , True) , inplace=True)\n",
    "    tmptable.reset_index(drop=True,inplace=True)\n",
    "    tmptable[['time']] = tmptable[['time']].applymap(lambda x : x[11:13])\n",
    "    tmptable['seq']=tmptable.apply(toSeq , axis=1)\n",
    "    tmptable = tmptable.groupby(['carnum']).apply(merseq)\n",
    "    tmptable = pd.DataFrame(tmptable)\n",
    "    tmptable.reset_index(inplace = True)\n",
    "    tmptable.rename(columns={0:table},inplace = True)\n",
    "    FirStaOD[table] = tmptable\n",
    "    del tmptable\n",
    "    gc.collect()\n",
    "    print(table , \" has query successfully!\")\n",
    "cur.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "FirStaOD['20191227'].head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# 把第一阶段各天的sequence合并到一张dataframe中去\n",
    "FirStaTab=FirStaOD['20191217']\n",
    "tablenames = ['20191218','20191220','20200106','20200110','20191223','20191224','20191225','20191226','20191227']\n",
    "for table in tablenames:\n",
    "    tmptable = FirStaOD[table]\n",
    "    FirStaTab = pd.merge(FirStaTab,tmptable,how='outer',on='carnum')\n",
    "\n",
    "# 将FirStaOD释放掉\n",
    "del FirStaOD\n",
    "gc.collect()\n",
    "\n",
    "# 把出现天数少于5天的车辆删除，因为我取5天为阈值，support大于等于5方才认为频繁出行模式，因此少于5天的车不可能有频繁出行模式。\n",
    "# 该步骤主要为了减少计算量和存储量\n",
    "def judgedel(ser):\n",
    "    # 判断该行是否需要删除\n",
    "    if len(ser[ser.isnull()]) > 5:\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "\n",
    "FirStaTab['tag'] = FirStaTab.apply(judgedel , axis = 1)\n",
    "FirStaTab = FirStaTab[FirStaTab['tag'] == 0]\n",
    "FirStaTab.drop(columns=['tag'],inplace=True)\n",
    "FirStaTab.reset_index(drop=True,inplace=True)\n",
    "FirStaTab.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2.第一阶段frequent sequential activity pattern mining"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "本cell是用prefix span algorithm进行frequent sequence activity pattern mining的算法实现，\n",
    "并且对两个item是否相同的定义做了修改，只要两个item时间绝对值差1以内，空间直线距离在1km以内，则都认为是相同的item。\n",
    "算法对空间和时间复杂度考虑较少，因为本研究用该算法挖掘出一个人的在一段时间的frequent sequence activity pattern mining，\n",
    "因此对于每个人来说数据量都极少。\n",
    "算法已经经过若干测试均是正确的，该算法将sequence长度为1的pattern删除，仅保留长度大于等于2的pattern，\n",
    "因为频繁模式挖掘过程中对出行链长度过长的出行者做了出行链压缩，此时早高峰出行可能仅保留为一个点A，晚高峰出行仅保留一个点B，\n",
    "即长度为2可能为{A,B}的形式，这可能是{home->work,work->home}的形式，而长度为1则出行信息过少，直接删除。\n",
    "'''\n",
    "# 定义通过经纬度计算直线距离的函数\n",
    "from math import radians, cos, sin, asin, sqrt\n",
    "def geodistance(lng1,lat1,lng2,lat2):\n",
    "    if lng1 == None or lng2 == None or lat1 == None or lat2 == None:\n",
    "        return 999999\n",
    "    elif (lng1 < 119 or lng1 > 121) or (lng2 < 119 or lng2 > 121) or (lat1 < 29 or lat1 > 30) or (lat2 < 29 or lat2 > 30):\n",
    "        return 999999\n",
    "    lng1 = float(lng1) ; lng2 = float(lng2) ; lat1 = float(lat1) ; lat2 = float(lat2)\n",
    "    lng1,lat1,lng2,lat2 = map(radians,[lng1,lat1,lng2,lat2])\n",
    "    dlon = lng2-lng1\n",
    "    dlat = lat2-lat1\n",
    "    a = sin(dlat/2)**2 + cos(lat1) * cos(lat2) * sin(dlon/2)**2\n",
    "    distance = 2* asin(sqrt(a))*6371*1000\n",
    "    distance = round(distance/1000,3)\n",
    "    return distance\n",
    "\n",
    "def issame(l1,l2):\n",
    "    timetag=0 ; spacetag=0\n",
    "    if (abs(int(l1[0]) - int(l2[0])) <= 1) :\n",
    "        timetag=1\n",
    "    if (geodistance(l1[1],l1[2],l2[1],l2[2])<=1) :\n",
    "        spacetag=1\n",
    "    if (timetag + spacetag) == 2:\n",
    "        return True\n",
    "    else: \n",
    "        return False\n",
    "\n",
    "# def issame(l1,l2):\n",
    "#     写代码做case时的判断item是否相同的函数\n",
    "#     timetag=0 ; spacetag=0\n",
    "#     if abs(int(l1[0]) - int(l2[0])) <= 1 :\n",
    "#         timetag=1\n",
    "#     if abs((l1[1]) - int(l2[1])) <= 1 and abs(int(l1[2]) - int(l2[2])) <= 1:\n",
    "#         spacetag=1\n",
    "#     if (timetag + spacetag) == 2:\n",
    "#         return True\n",
    "#     else: \n",
    "#         return False\n",
    "\n",
    "def calsup(D ,i,j):\n",
    "    sup=1  # 初始化D[i][j]的support\n",
    "    e = D[i][j]\n",
    "    postfix = []  # 初始化后缀数据库，建立D[i][j]的后缀数据库\n",
    "    for indexi in range(len(D)):\n",
    "        if indexi == i:\n",
    "            pass\n",
    "        else:\n",
    "            for indexj in range(len(D[indexi])):\n",
    "                if issame(e , D[indexi][indexj]):\n",
    "                    sup += 1\n",
    "                    postfix.append((indexi,indexj))\n",
    "                    break\n",
    "                else:\n",
    "                    pass\n",
    "    return sup,postfix\n",
    "\n",
    "def calsupkb2(D,i,j,postfix):\n",
    "    # D[i][j]是需要添加在频繁k-1项集后面的元素\n",
    "    # postfix是频繁k-1项的后缀投影库\n",
    "    sup=1  # 初始化D[i][j]的support\n",
    "    e = D[i][j]\n",
    "    postfix2 = []  # 初始化后缀数据库，建立D[i][j]的后缀数据库\n",
    "    for indexi in range(len(postfix)):\n",
    "        postindex=postfix[indexi]\n",
    "        if postindex[1] >= (len(D[postindex[0]])-1):\n",
    "            pass\n",
    "        else:\n",
    "            for indexj in range(postindex[1]+1 , len(D[postindex[0]])):\n",
    "                if issame(e , D[postindex[0]][indexj]):\n",
    "                    sup+=1\n",
    "                    postfix2.append((postindex[0],indexj))\n",
    "                    break\n",
    "                else:\n",
    "                    pass\n",
    "    \n",
    "    return sup,postfix2\n",
    "\n",
    "def rmdup(freqseq):\n",
    "    # 将得到的频繁项集中重复的以及长度仅为1的元素去除\n",
    "    # 去除长度为1的pattern\n",
    "    needdelind = []\n",
    "    for i in range(len(freqseq)):\n",
    "        if len(freqseq[i][0:-3]) < 2:\n",
    "            needdelind.append(i)\n",
    "    # 将长度小于2的项删除\n",
    "    for i in range(len(needdelind)):\n",
    "        del freqseq[needdelind[len(needdelind)-i-1]]\n",
    "    \n",
    "    # 重复的pattern仅保留一项\n",
    "    freqlen = len(freqseq)\n",
    "    needdelind = []\n",
    "    for i in range(freqlen):\n",
    "        for j in range(0,i):\n",
    "            if freqseq[i][0:-3] == freqseq[j][0:-3]:\n",
    "                needdelind.append(i)\n",
    "                break\n",
    "    for i in range(len(needdelind)):\n",
    "        del freqseq[needdelind[len(needdelind)-i-1]]\n",
    "    \n",
    "    return freqseq\n",
    "\n",
    "def prefix_main(E , min_sup):\n",
    "    # E为sequence矩阵，每一行是一个sequence\n",
    "    # min_sup是最小支持度\n",
    "    '''\n",
    "    输入case：\n",
    "    p1 = [['07',1,1], \n",
    "          ['08',3,3],\n",
    "          ['16',4,4,],\n",
    "          ['17',4,4,]]\n",
    "    p2 = [['07',1,1], \n",
    "          ['08',3,3],\n",
    "          ['16',4,4,],\n",
    "          ['17',4,4,]]\n",
    "    p3 = [['07',1,1], \n",
    "          ['08',3,3],\n",
    "          ['16',4,4,],\n",
    "          ['17',4,4,]]\n",
    "\n",
    "    E=[p1,p2,p3] \n",
    "    prefix_main(E , min_sup=3)\n",
    "    \n",
    "    E如上图所示，其中真实数据需要将1,1,3,3,4,4等换为对应的经纬度\n",
    "    输出case：\n",
    "    [[['07', 1, 1], ['08', 3, 3], ['16', 4, 4], (0, 2), 3, [(1, 2), (2, 2)]],\n",
    "     [['07', 1, 1], ['08', 3, 3], ['17', 4, 4], (0, 3), 3, [(1, 2), (2, 2)]],\n",
    "     [['07', 1, 1], ['16', 4, 4], ['17', 4, 4], (0, 3), 3, [(1, 3), (2, 3)]],\n",
    "     [['08', 3, 3], ['16', 4, 4], ['17', 4, 4], (0, 3), 3, [(1, 3), (2, 3)]],\n",
    "     [['07', 1, 1],\n",
    "      ['08', 3, 3],\n",
    "      ['16', 4, 4],\n",
    "      ['17', 4, 4],\n",
    "      (0, 3),\n",
    "      3,\n",
    "      [(1, 3), (2, 3)]]]\n",
    "    输出是一个list，list中每个元素代表一个pattern，如\n",
    "    [['07', 1, 1], ['08', 3, 3], ['16', 4, 4], (0, 2), 3, [(1, 2), (2, 2)]]就是一种pattern，\n",
    "    其中['07', 1, 1], ['08', 3, 3], ['16', 4, 4]代表该pattern的详细信息，\n",
    "      (0,2)代表该pattern的最后一个item即['16', 4, 4]位于输入的（0,2）位置即E[0][2]；\n",
    "      3代表该pattern的support为3；\n",
    "      [(1, 2), (2, 2)]]代表除了自身外，support的另外两个贡献的最后一个sequence在输入中的位置，\n",
    "      即后缀投影数据库最后一个元素在输入中的位置。\n",
    "    '''\n",
    "    \n",
    "    freqresult=[] # 存储最终的频繁1项集，频繁2项集，频繁3项集···\n",
    "    freqk_1 = [] # 存储频繁k-1项集\n",
    "    # 生成初始的频繁1项集\n",
    "    for i in range(len(E)):\n",
    "        for j in range(len(E[i])):\n",
    "            sup , postfix = calsup(E,i,j)  # 计算E[i][j]的support\n",
    "            if sup >= min_sup:\n",
    "                freqk_1.append([E[i][j],(i,j),sup,postfix])  # 如果是频繁项集，就以[项,支持度,(该频繁项的最后一个元素所在下标)]的形式添加到freqk中去\n",
    "    freqresult.extend(freqk_1)\n",
    "#     print(len(freqresult))\n",
    "    # 生成频繁k项集\n",
    "    k=2  # 已获得频繁k-1项集\n",
    "    while True:\n",
    "#         print(\"已经生成频繁{0}项集\".format(k))\n",
    "        freqk = []  #存放频繁k项集\n",
    "        for indexi in range(len(freqk_1)):\n",
    "            stmp = freqk_1[indexi]\n",
    "            if stmp[-3][1] >= (len(E[stmp[-3][0]])-1) :\n",
    "                pass\n",
    "            else:\n",
    "                for indexm in range(stmp[-3][1]+1 , len(E[stmp[-3][0]])):\n",
    "    #                 stmp2 = E[stmp[-3][0]][indexm]  # 得到想要添加在频繁k-1项集后的元素\n",
    "                    sup , postfix = calsupkb2(E,stmp[-3][0],indexm,stmp[-1])\n",
    "                    if sup >= min_sup:\n",
    "                        freqk.append(stmp[0:-3] + [E[stmp[-3][0]][indexm],(stmp[-3][0],indexm),sup,postfix])\n",
    "#         print('频繁k项集个数为：',len(freqk))\n",
    "        if len(freqk) == 0:\n",
    "            break\n",
    "        else:\n",
    "            freqresult.extend(freqk)\n",
    "            freqk_1 = copy.deepcopy(freqk)\n",
    "            k+=1\n",
    "    freqresult = rmdup(freqresult)\n",
    "    return freqresult"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "频繁模式挖掘算法复杂度过高，运行了5个小时，连一万行都没有处理完，因此删除掉无用数据，在进行频繁模式的挖掘。\n",
    "\n",
    "进一步分析了运行速度这么慢的原因，主要是因为prefix_main函数的复杂度会随着出行链的长度出现阶层级上升的情况，因此如果有很长的出行链，如超过20个点位，则7项集的频繁模式就可能需要遍历几十万次。这边用一个实例做一个说明，假设有一个出行者，第一阶段5天都有出行，且每天的出行链均相同，长度为20，则一项集模式有100个，二项集模式有$C_{20}^2*5=950$个，三项集模式有$C_{20}^3*5=5700$个，四项集模式为$C_{20}^4*5=24225$个，依次类推，可以知道7项集有387600个，而且随着出行链长度增长，数量级上升很快，这使得出行链长度长，并且出行者出行规律时无法得到结果，实际过程中，一个出行链长度为25的车辆，5天均出行，出行行为很规律，运行了10个小时没有给出结果。因此针对此问题，需要对出行者的出行链进行压缩，以使得该算法可以在指定时间得到结果。\n",
    "\n",
    "出行链压缩算法方法如下：出行者一天的行程一小时仅保留support最大的那个点位，即一小时仅保留一个点位信息，如此一来一个出行者一天出行链最长仅24个点位。代码编写思路为：首先，遍历第一天的出行链，若相邻两个点位是处于同一个小时被检测到的，则保留两个点位中support大的那个，删除support小的那个点位。\n",
    "\n",
    "并不是对所有出行者都做出行链压缩，只有当出行者的出行链过长时才做出行链压缩，具体来说，当出行者至少有min_sup天出行链长度大于等于10时，才做出行链压缩，因此出行者出行链最长也仅仅为9，此时可以控制prefix span的遍历次数最多时为$C_{9}^4*5=630$次，可以接受。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rmdata(ser , min_sup=5):\n",
    "    # 对数据进行预处理及压缩，包括：\n",
    "    # 1.一周出行少于3天的车辆标记为0，后续直接删除；\n",
    "    # 2.一周出行大于等于3天的车辆需要进行压缩，出行者一天的行程一小时仅保留support最大的那个点位，即一小时仅保留一个点位信息，\n",
    "    # 如此一来一个出行者一天出行链最长仅24个点位。\n",
    "    # 3.但是对于一些出行行为类似于出租车的车辆，经过压缩处理后，可能依然5天均有出行，且出行链长度大于16，\n",
    "    # 这类车辆会造成prefix span算法爆炸，对于这类车辆，采取手段为保留前5个点位和最后4个点位，舍弃中间点位，因为首末点位对车辆出行行为刻画更为有意义。\n",
    "#     if ser.name%10000 == 0:\n",
    "#         print(\"已经处理到第{0}行.\".format(ser.name))\n",
    "#     ser = ser.dropna()\n",
    "#     ser = list(ser)\n",
    "#     ser = ser[1:]\n",
    "    if len(ser) < min_sup:\n",
    "        return 0   # 0标签的含义是删除掉这个出行者\n",
    "    else:\n",
    "        for i in range(len(ser)):\n",
    "            j=0\n",
    "            while j < len(ser[i]):\n",
    "                sup , _ = calsup(ser ,i,j)\n",
    "                if sup < min_sup:\n",
    "                    del ser[i][j]\n",
    "                else:\n",
    "                    if j == 0 :\n",
    "                        j+=1\n",
    "                    else:\n",
    "                        if ser[i][j][0] != ser[i][j-1][0]:  # 如果后一个点和前一个点不在同一个小时，则遍历后一个元素；\n",
    "                            j+=1\n",
    "                        else:\n",
    "                            sup , _ = calsup(ser ,i,j) ; sup_1,_=calsup(ser ,i,j-1)\n",
    "                            if sup <= sup_1:  # 相邻元素中删除掉sup较小的那一个\n",
    "                                del ser[i][j]\n",
    "                            else:\n",
    "                                del ser[i][j-1]\n",
    "        \n",
    "        tag1=0 # 标记有多少天的出行链长度大于等于10\n",
    "        tag2=0 # 标记有多少天的出行链长度大于等于2\n",
    "        for i in range(len(ser)):\n",
    "            if len(ser[i]) >= 10:\n",
    "                tag1 += 1\n",
    "            if len(ser[i])>=2:\n",
    "                tag2+=1\n",
    "        if tag1>=min_sup:\n",
    "            # 对于长度大于等于10的出行链，仅保留前5个点和最后4个点\n",
    "            for i in range(len(ser)):\n",
    "                if len(ser[i]) >= 10:\n",
    "                    while len(ser[i]) >= 10:\n",
    "                        del ser[i][5]\n",
    "        if tag2 < min_sup:\n",
    "            return 0\n",
    "        \n",
    "        return 1  # 1标签意味着保留该出行者"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getfreqpattern(ser,min_sup=5):\n",
    "    # 对每一行使用这个func时会直接改变原始dataframe中每一行的元素，因为在本研究中dataframe中存储的是list的引用，因此虽然\n",
    "    # 深拷贝了dataframe，dataframe中的元素依然是list的引用，对list里的元素处理时依然处理的是原始的list中的数据，\n",
    "    # 因此会直接改变原始dataframe中list中的元素,详细原因见我的博客：https://blog.csdn.net/qq_39805362/article/details/113060958.\n",
    "#     ser = ser_copy.copy(deep=True)\n",
    "    # 得到traveler的frequent pattern\n",
    "    if ser.name%5000 == 0:\n",
    "        time_end = time.time()\n",
    "        consumedtime = time_end - time_begin\n",
    "        print(\"已经处理到第{0}行,共耗时{1}分钟.\".format(ser.name , round(consumedtime/60 , 3)))\n",
    "\n",
    "#     time_end = time.time()\n",
    "#     consumedtime = time_end - time_begin\n",
    "#     print(\"已经处理到第{0}行,共耗时{1}分钟.\".format(ser.name , round(consumedtime/60 , 3)))\n",
    "    \n",
    "    ser = ser.dropna()\n",
    "    ser = list(ser)\n",
    "    ser = ser[1:]\n",
    "    if len(ser) < min_sup:\n",
    "        return []\n",
    "    \n",
    "    tag1=0 # 标记有多少天的出行链长度大于等于10\n",
    "    tag2=0 # 标记有多少天的出行链长度大于等于2\n",
    "    totlen = 0 # 记录该阶段被检测总次数\n",
    "    for i in range(len(ser)):\n",
    "        totlen = totlen + len(ser[i])\n",
    "        if len(ser[i]) >= 10:\n",
    "            tag1 += 1\n",
    "        if len(ser[i])>=2:\n",
    "            tag2+=1\n",
    "    if tag1>=min_sup:\n",
    "        # 如果有大于min_sup天出行链长度大于等于10，则对出行链进行压缩\n",
    "        tmp = rmdata(ser , min_sup=min_sup)\n",
    "        if tmp == 0:\n",
    "            return []\n",
    "    elif totlen >= 10 * len(ser):\n",
    "        # 虽然少于min_sup天出行链长度大于等于10，但是有可能出现某一天出行链长度特别长，如20，而其他天出行链长度均只有9的情况出现\n",
    "        # 在这种情况下，依然会出现阶层级爆炸的情况，因此也需要做出行链压缩.\n",
    "        tmp = rmdata(ser , min_sup=min_sup)\n",
    "        if tmp == 0:\n",
    "            return []\n",
    "    if tag2 < min_sup:\n",
    "        return []\n",
    "    \n",
    "    # 注释部分的作用是：如果先保存前面跑出的每个人一周的出行链数据到本地，则dataframe中存储的list格式的元素会变成字符串格式，\n",
    "    # 此时，直接运行会出bug，因此便需要利用下面的注释部分重新将dataframe中存储的字符串格式的list转化为真正的list格式数据。\n",
    "#     multidaytrip=[]  # 存储一个人多日的trip chain\n",
    "#     for i in range(len(ser)):\n",
    "#         tmp = ser[i].split(',')\n",
    "#         tripchain = []  # 存储某个人一天的trip chain，形式是二维list的形式\n",
    "#         index = 0\n",
    "#         while index <= len(tmp) - 1:\n",
    "#             if index % 6 == 0:\n",
    "#                 tmp_list=[]  # 存储某一个人一次trip的OD信息\n",
    "#                 ele = tmp[index]\n",
    "#                 tmp_list.append(ele[3:-1])\n",
    "#             elif index%6==1 or index%6==2 or  index%6==4:\n",
    "#                 tmp_list.append(float(tmp[index]))\n",
    "#             elif index%6==3:\n",
    "#                 tmp_list.append(tmp[index][2:-1])\n",
    "#             elif index%6==5:\n",
    "#                 ele = tmp[index]\n",
    "#                 tmp_list.append(float(ele[0:-2]))\n",
    "#                 tripchain.append(tmp_list)\n",
    "#             index += 1\n",
    "# #         print(tripchain)\n",
    "#         multidaytrip.append(tripchain)\n",
    "\n",
    "    freqseq = prefix_main(ser , min_sup)\n",
    "    return freqseq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# 验证代码的正确性\n",
    "# 用自己的一个case测试一下代码正确性\n",
    "time_begin = time.time()\n",
    "case = copy.deepcopy(FirStaTab.iloc[0:3,:])\n",
    "case.loc[3] = ['我增加的一行',\n",
    "           [['18', 119.928, 29.2736], ['19', 119.92, 29.2649], ['21', 119.928, 29.2732]],\n",
    "           [['18', 119.928, 29.2736], ['19', 119.92, 29.2649], ['21', 119.928, 29.2732]],\n",
    "           [['18', 119.928, 29.2736], ['19', 119.92, 29.2649], ['21', 119.928, 29.2732]],\n",
    "           [['18', 119.928, 29.2736], ['19', 119.92, 29.2649], ['21', 119.928, 29.2732]],\n",
    "           [['18', 119.928, 29.2736], ['19', 119.92, 29.2649], ['21', 119.928, 29.2732]],\n",
    "            None,None,None,None,None]\n",
    "res = case.apply(getfreqpattern,axis=1)\n",
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "res[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# 再来一个出行链超长的出行者做一个case\n",
    "# 检验上面cell写的数据压缩算法的正确性\n",
    "case = FirStaTab.iloc[0:3 , :]\n",
    "case.loc[3]=['我增加的一行',\n",
    "           [['01', 119.928, 29.2736],['02', 119.928, 29.2736],['03', 119.928, 29.2736],['04', 119.928, 29.2736],['05', 119.928, 29.2736],['06', 119.928, 29.2736],['07', 119.928, 29.2736],['08', 119.928, 29.2736],['09', 119.928, 29.2736],['10', 119.928, 29.2736],['11', 119.928, 29.2736],['12', 119.928, 29.2736],['13', 119.928, 29.2736],['14', 119.928, 29.2736],['15', 119.928, 29.2736],['16', 119.928, 29.2736],['17', 119.928, 29.2736],['18', 119.928, 29.2736], ['19', 119.92, 29.2649], ['19', 10.92, 29.2649], ['19', 119.92, 29.2649], ['19', 119.92, 29.2649], ['21', 119.928, 29.2732]],\n",
    "           [['01', 119.928, 29.2736],['02', 119.928, 29.2736],['03', 119.928, 29.2736],['04', 119.928, 29.2736],['05', 119.928, 29.2736],['06', 119.928, 29.2736],['07', 119.928, 29.2736],['08', 119.928, 29.2736],['09', 119.928, 29.2736],['10', 119.928, 29.2736],['11', 119.928, 29.2736],['12', 119.928, 29.2736],['13', 119.928, 29.2736],['14', 119.928, 29.2736],['15', 119.928, 29.2736],['16', 119.928, 29.2736],['17', 119.928, 29.2736],['18', 119.928, 29.2736], ['19', 119.92, 29.2649], ['19', 10.92, 29.2649], ['19', 119.92, 29.2649], ['19', 119.92, 29.2649], ['21', 119.928, 29.2732]],\n",
    "           [['01', 119.928, 29.2736],['02', 119.928, 29.2736],['03', 119.928, 29.2736],['04', 119.928, 29.2736],['05', 119.928, 29.2736],['06', 119.928, 29.2736],['07', 119.928, 29.2736],['08', 119.928, 29.2736],['09', 119.928, 29.2736],['10', 119.928, 29.2736],['11', 119.928, 29.2736],['12', 119.928, 29.2736],['13', 119.928, 29.2736],['14', 119.928, 29.2736],['15', 119.928, 29.2736],['16', 119.928, 29.2736],['17', 119.928, 29.2736],['18', 119.928, 29.2736], ['19', 119.92, 29.2649], ['19', 10.92, 29.2649], ['19', 119.92, 29.2649], ['19', 119.92, 29.2649], ['21', 119.928, 29.2732]],\n",
    "           [['01', 119.928, 29.2736],['02', 119.928, 29.2736],['03', 119.928, 29.2736],['04', 119.928, 29.2736],['05', 119.928, 29.2736],['06', 119.928, 29.2736],['07', 119.928, 29.2736],['08', 119.928, 29.2736],['09', 119.928, 29.2736],['10', 119.928, 29.2736],['11', 119.928, 29.2736],['12', 119.928, 29.2736],['13', 119.928, 29.2736],['14', 119.928, 29.2736],['15', 119.928, 29.2736],['16', 119.928, 29.2736],['17', 119.928, 29.2736],['18', 119.928, 29.2736], ['19', 119.92, 29.2649], ['19', 10.92, 29.2649], ['19', 119.92, 29.2649], ['19', 119.92, 29.2649], ['21', 119.928, 29.2732]],\n",
    "           [['01', 119.928, 29.2736],['02', 119.928, 29.2736],['03', 119.928, 29.2736],['04', 119.928, 29.2736],['05', 119.928, 29.2736],['06', 119.928, 29.2736],['07', 119.928, 29.2736],['08', 119.928, 29.2736],['09', 119.928, 29.2736],['10', 119.928, 29.2736],['11', 119.928, 29.2736],['12', 119.928, 29.2736],['13', 119.928, 29.2736],['14', 119.928, 29.2736],['15', 119.928, 29.2736],['16', 119.928, 29.2736],['17', 119.928, 29.2736],['18', 119.928, 29.2736], ['19', 119.92, 29.2649], ['19', 10.92, 29.2649], ['19', 119.92, 29.2649], ['19', 119.92, 29.2649], ['21', 119.928, 29.2732]],\n",
    "            None,None,None,None,None]\n",
    "res = case.apply(getfreqpattern,axis=1)\n",
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "res[3][-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "time_begin = time.time()\n",
    "FirStaTab['freseq'] = FirStaTab.apply(getfreqpattern,axis=1)\n",
    "FirStaTab.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# 将得不到frequent sequence activity pattern 的人都去除掉\n",
    "def keepornot(x):\n",
    "    if len(x) == 0:\n",
    "        return None\n",
    "    else:\n",
    "        return x\n",
    "FirStaTab['freseq'] = FirStaTab[['freseq']].applymap(keepornot)\n",
    "FirStaTab = FirStaTab[FirStaTab['freseq'].notnull()]\n",
    "FirStaTab.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3 第一阶段阶段内regularity计算"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getLCS(s1,s2):\n",
    "    # 获得最长s1，s2最长公共子串的长度\n",
    "    len1 = len(s1) ; len2 = len(s2)\n",
    "    if len1 >= len2:\n",
    "        lcs = []  # 存储最长公共子串\n",
    "        startindex = 0  # s2中开始检索有无该item的下标起始位置\n",
    "        for i in range(len1):\n",
    "            tmp = s1[i]\n",
    "            # 去s2中找有没有tmp\n",
    "            for j in range(startindex , len2):\n",
    "                if issame(s2[j],tmp):\n",
    "                    lcs.append(tmp)\n",
    "                    startindex = j+1\n",
    "                    break\n",
    "        return len(lcs)\n",
    "    else:\n",
    "        lcs = []  # 存储最长公共子串\n",
    "        startindex = 0\n",
    "        for i in range(len2):\n",
    "            tmp=s2[i]\n",
    "            for j in range(startindex,len1):\n",
    "                if issame(s1[j],tmp):\n",
    "                    lcs.append(tmp)\n",
    "                    startindex = j+1\n",
    "                    break\n",
    "        return len(lcs)\n",
    "   \n",
    "# test case\n",
    "getLCS([['07', 120.086, 29.3184], ['19', 120.058, 29.3415]] ,\n",
    "       [['06', 120.091, 29.3107], ['19', 130.058, 29.3415]] )  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pattern_simi(p1,p2):\n",
    "    # 计算pattern1和pattern2的similarity\n",
    "    lenp1 = len(p1)\n",
    "    lenp2 = len(p2)\n",
    "    lenLCS = getLCS(p1,p2)\n",
    "    patsimi = (lenLCS+lenLCS) / (lenp1 + lenp2)\n",
    "    return patsimi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def intraweight(ser):\n",
    "    # 计算阶段内的intra person regularity\n",
    "    ser = ser.dropna() # 删除空值\n",
    "    ser = list(ser)\n",
    "    ser = ser[1:-1]\n",
    "    weig = 0\n",
    "    total_simi = 0\n",
    "    for i in range(len(ser)):\n",
    "        for j in range(len(ser)):\n",
    "            if len(ser[i]) != 0 and len(ser[j])!=0:\n",
    "                tmp = pattern_simi(ser[i],ser[j])\n",
    "                total_simi = total_simi+tmp\n",
    "                weig += 1\n",
    "    return (total_simi / weig)\n",
    "\n",
    "# a test case\n",
    "# sercase = FirStaTab.iloc[3,:]\n",
    "# print(sercase)\n",
    "# intraweight(sercase)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**第一阶段内出行者regularity计算，并存为regu字段**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "FirStaTab['regu'] = FirStaTab.apply(intraweight , axis = 1)\n",
    "FirStaTab.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "FirStaTab = FirStaTab[['carnum','freseq','regu']]\n",
    "FirStaTab.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "FirStaTab.to_csv('FirStaTab.csv',encoding = 'gbk',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "len(FirStaTab)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4.第二阶段trip挖掘,frequent sequence pattern mining,regularity,similarity计算"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 统计第二阶段各天每辆车每次trip的OD的时空信息\n",
    "conn.ping(reconnect=True)\n",
    "cur = conn.cursor()\n",
    "tablenames = ['20200210','20200211','20200212','20200213','20200214','20200217','20200218','20200219','20200220','20200221']\n",
    "SecStaOD = dict()  # 用一个字典来存储第二阶段各天的每辆车每次trip的OD的时空信息\n",
    "for table in tablenames:\n",
    "    sql = '''\n",
    "    select carnum1 , carnum2 , time , LNG , LAT from {}aj2 where (type = 2 or type = 52) and (lng is not null and lat is not null and \n",
    "    lng > 119 and lng < 121 and lat > 29 and lat < 30)\n",
    "    '''.format(table)\n",
    "    cur.execute(sql)\n",
    "    conn.commit()\n",
    "    tmptable = cur.fetchall()\n",
    "    tmptable = pd.DataFrame(tmptable)\n",
    "    tmptable.rename(columns = {0:'carnum1' , 1:'carnum2' , 2:'time' , 3:'LNG' , 4:'LAT'} , inplace = True)\n",
    "    tmptable['carnum'] = tmptable['carnum1'] + tmptable['carnum2']\n",
    "    # 得到车辆每一天的出行链\n",
    "    tmptable.sort_values(by = ['carnum' , 'time'] , ascending = (True , True) , inplace=True)\n",
    "    tmptable.reset_index(drop=True,inplace=True)\n",
    "    tmptable[['time']] = tmptable[['time']].applymap(lambda x : x[11:13])\n",
    "    tmptable['seq']=tmptable.apply(toSeq , axis=1)\n",
    "    tmptable = tmptable.groupby(['carnum']).apply(merseq)\n",
    "    tmptable = pd.DataFrame(tmptable)\n",
    "    tmptable.reset_index(inplace = True)\n",
    "    tmptable.rename(columns={0:table},inplace = True)\n",
    "    SecStaOD[table] = tmptable\n",
    "    del tmptable\n",
    "    gc.collect()\n",
    "    print(table , \" has query successfully!\")\n",
    "cur.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "SecStaOD['20200210'].head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 把第一阶段各天的sequence合并到一张dataframe中去\n",
    "SecStaTab=SecStaOD['20200210']\n",
    "tablenames = ['20200211','20200212','20200213','20200214','20200217','20200218','20200219','20200220','20200221']\n",
    "for table in tablenames:\n",
    "    tmptable = SecStaOD[table]\n",
    "    SecStaTab = pd.merge(SecStaTab,tmptable,how='outer',on='carnum')\n",
    "\n",
    "# 将FirStaOD释放掉\n",
    "del SecStaOD\n",
    "gc.collect()\n",
    "\n",
    "# 把出现天数少于5天的车辆删除，因为我取5天为阈值，support大于等于5方才认为频繁出行模式，因此少于5天的车不可能有频繁出行模式。\n",
    "# 该步骤主要为了减少计算量和存储量\n",
    "def judgedel(ser):\n",
    "    # 判断该行是否需要删除\n",
    "    if len(ser[ser.isnull()]) > 5:\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "\n",
    "SecStaTab['tag'] = SecStaTab.apply(judgedel , axis = 1)\n",
    "SecStaTab = SecStaTab[SecStaTab['tag'] == 0]\n",
    "SecStaTab.drop(columns=['tag'],inplace=True)\n",
    "SecStaTab.reset_index(drop=True,inplace=True)\n",
    "SecStaTab.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "time_begin = time.time()\n",
    "SecStaTab['freseq'] = SecStaTab.apply(getfreqpattern,axis=1)\n",
    "SecStaTab.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# 将得不到frequent sequence activity pattern 的人都去除掉\n",
    "def keepornot(x):\n",
    "    if len(x) == 0:\n",
    "        return None\n",
    "    else:\n",
    "        return x\n",
    "SecStaTab['freseq'] = SecStaTab[['freseq']].applymap(keepornot)\n",
    "SecStaTab = SecStaTab[SecStaTab['freseq'].notnull()]\n",
    "SecStaTab.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# 第二阶段regularity地计算\n",
    "SecStaTab['regu'] = SecStaTab.apply(intraweight , axis = 1)\n",
    "SecStaTab.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "SecStaTab = SecStaTab[['carnum','freseq','regu']]\n",
    "SecStaTab.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "SecStaTab.to_csv('SecStaTab.csv',encoding = 'gbk',index=False)\n",
    "len(SecStaTab)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5.第三阶段trip挖掘,frequent sequence pattern mining,regularity,similarity计算（原来的第三阶段不再运行，因为现在总共只需要划分为三个阶段，原第四阶段现在更改为第三阶段）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # 统计第三阶段各天每辆车每次trip的OD的时空信息\n",
    "# conn.ping(reconnect=True)\n",
    "# cur = conn.cursor()\n",
    "# sql = '''\n",
    "#     select carnum1 , carnum2 , concat(carnum1 , carnum2) as carnum from smalocalizedvehicles\n",
    "#     '''\n",
    "# cur.execute(sql)\n",
    "# conn.commit()\n",
    "# localveh = cur.fetchall()\n",
    "# localveh = pd.DataFrame(localveh)\n",
    "# localveh.rename(columns = {0:'carnum1' , 1:'carnum2' , 2:'carnum'} , inplace = True)\n",
    "# tablenames = ['20200316','20200317','20200318','20200319','20200320']\n",
    "# ThiStaOD = dict()  # 用一个字典来存储第一阶段各天的每辆车每次trip的OD的时空信息\n",
    "# for table in tablenames:\n",
    "#     sql = '''\n",
    "#     select carnum1 , carnum2 , time , LNG , LAT from {}aj where (type = 2 or type = 52) and (lng is not null and lat is not null and \n",
    "#     lng > 119 and lng < 121 and lat > 29 and lat < 30)\n",
    "#     '''.format(table)\n",
    "#     cur.execute(sql)\n",
    "#     conn.commit()\n",
    "#     tmptable = cur.fetchall()\n",
    "#     tmptable = pd.DataFrame(tmptable)\n",
    "#     tmptable.rename(columns = {0:'carnum1' , 1:'carnum2' , 2:'time' , 3:'LNG' , 4:'LAT'} , inplace = True)\n",
    "#     tmptable = pd.merge(tmptable , localveh , how = 'inner' , on = ['carnum1' , 'carnum2'])\n",
    "    \n",
    "#     # 得到车辆每一天的出行链\n",
    "#     tmptable.sort_values(by = ['carnum' , 'time'] , ascending = (True , True) , inplace=True)\n",
    "#     tmptable.reset_index(drop=True,inplace=True)\n",
    "#     tmptable[['time']] = tmptable[['time']].applymap(lambda x : x[11:13])\n",
    "#     tmptable['seq']=tmptable.apply(toSeq , axis=1)\n",
    "#     tmptable = tmptable.groupby(['carnum']).apply(merseq)\n",
    "#     tmptable = pd.DataFrame(tmptable)\n",
    "#     tmptable.reset_index(inplace = True)\n",
    "#     tmptable.rename(columns={0:table},inplace = True)\n",
    "#     ThiStaOD[table] = tmptable\n",
    "#     print(table , \" has query successfully!\")\n",
    "# cur.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ThiStaOD['20200316']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# # 把第一阶段各天的sequence合并到一张dataframe中去\n",
    "# ThiStaTab=ThiStaOD['20200316']\n",
    "# tablenames = ['20200317','20200318','20200319','20200320']\n",
    "# for table in tablenames:\n",
    "#     tmptable = ThiStaOD[table]\n",
    "#     ThiStaTab = pd.merge(ThiStaTab,tmptable,how='outer',on='carnum')\n",
    "# ThiStaTab.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# time_begin = time.time()\n",
    "# ThiStaTab['freseq'] = ThiStaTab.apply(getfreqpattern,axis=1)\n",
    "# ThiStaTab.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# # 将得不到frequent sequence activity pattern 的人都去除掉\n",
    "# def keepornot(x):\n",
    "#     if len(x) == 0:\n",
    "#         return None\n",
    "#     else:\n",
    "#         return x\n",
    "# ThiStaTab['freseq'] = ThiStaTab[['freseq']].applymap(keepornot)\n",
    "# ThiStaTab = ThiStaTab[ThiStaTab['freseq'].notnull()]\n",
    "# ThiStaTab.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# len(ThiStaTab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # 第三阶段regularity地计算\n",
    "# ThiStaTab['regu'] = ThiStaTab.apply(intraweight , axis = 1)\n",
    "# ThiStaTab.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ThiStaTab = ThiStaTab[['carnum','freseq','regu']]\n",
    "# ThiStaTab.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # 释放第三阶段一些用不到的内存\n",
    "# import gc\n",
    "# del ThiStaOD\n",
    "# gc.collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6.第三阶段（原第四阶段）trip挖掘,frequent sequence pattern mining,regularity,similarity计算"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 统计第三（原第四）阶段各天每辆车每次trip的OD的时空信息\n",
    "conn.ping(reconnect=True)\n",
    "cur = conn.cursor()\n",
    "tablenames = ['20200608','20200609','20200610','20200611','20200612','20200615','20200616','20200617','20200618','20200619']\n",
    "FouStaOD = dict()  # 用一个字典来存储第二阶段各天的每辆车每次trip的OD的时空信息\n",
    "for table in tablenames:\n",
    "    sql = '''\n",
    "    select carnum1 , carnum2 , time , LNG , LAT from {}aj2 where (type = 2 or type = 52) and (lng is not null and lat is not null and \n",
    "    lng > 119 and lng < 121 and lat > 29 and lat < 30)\n",
    "    '''.format(table)\n",
    "    cur.execute(sql)\n",
    "    conn.commit()\n",
    "    tmptable = cur.fetchall()\n",
    "    tmptable = pd.DataFrame(tmptable)\n",
    "    tmptable.rename(columns = {0:'carnum1' , 1:'carnum2' , 2:'time' , 3:'LNG' , 4:'LAT'} , inplace = True)\n",
    "    tmptable['carnum'] = tmptable['carnum1'] + tmptable['carnum2']\n",
    "    # 得到车辆每一天的出行链\n",
    "    tmptable.sort_values(by = ['carnum' , 'time'] , ascending = (True , True) , inplace=True)\n",
    "    tmptable.reset_index(drop=True,inplace=True)\n",
    "    tmptable[['time']] = tmptable[['time']].applymap(lambda x : x[11:13])\n",
    "    tmptable['seq']=tmptable.apply(toSeq , axis=1)\n",
    "    tmptable = tmptable.groupby(['carnum']).apply(merseq)\n",
    "    tmptable = pd.DataFrame(tmptable)\n",
    "    tmptable.reset_index(inplace = True)\n",
    "    tmptable.rename(columns={0:table},inplace = True)\n",
    "    FouStaOD[table] = tmptable\n",
    "    del tmptable\n",
    "    gc.collect()\n",
    "    print(table , \" has query successfully!\")\n",
    "cur.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "FouStaOD['20200608'].head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 把第四阶段各天的sequence合并到一张dataframe中去\n",
    "FouStaTab=FouStaOD['20200608']\n",
    "tablenames = ['20200609','20200610','20200611','20200612','20200615','20200616','20200617','20200618','20200619']\n",
    "for table in tablenames:\n",
    "    tmptable = FouStaOD[table]\n",
    "    FouStaTab = pd.merge(FouStaTab,tmptable,how='outer',on='carnum')\n",
    "\n",
    "# 将FirStaOD释放掉\n",
    "del FouStaOD\n",
    "gc.collect()\n",
    "\n",
    "# 把出现天数少于5天的车辆删除，因为我取5天为阈值，support大于等于5方才认为频繁出行模式，因此少于5天的车不可能有频繁出行模式。\n",
    "# 该步骤主要为了减少计算量和存储量\n",
    "def judgedel(ser):\n",
    "    # 判断该行是否需要删除\n",
    "    if len(ser[ser.isnull()]) > 5:\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "\n",
    "FouStaTab['tag'] = FouStaTab.apply(judgedel , axis = 1)\n",
    "FouStaTab = FouStaTab[FouStaTab['tag'] == 0]\n",
    "FouStaTab.drop(columns=['tag'],inplace=True)\n",
    "FouStaTab.reset_index(drop=True,inplace=True)\n",
    "FouStaTab.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "time_begin = time.time()\n",
    "FouStaTab['freseq'] = FouStaTab.apply(getfreqpattern,axis=1)\n",
    "FouStaTab.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 将得不到frequent sequence activity pattern 的人都去除掉\n",
    "def keepornot(x):\n",
    "    if len(x) == 0:\n",
    "        return None\n",
    "    else:\n",
    "        return x\n",
    "FouStaTab['freseq'] = FouStaTab[['freseq']].applymap(keepornot)\n",
    "FouStaTab = FouStaTab[FouStaTab['freseq'].notnull()]\n",
    "FouStaTab.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# 第四阶段regularity地计算\n",
    "FouStaTab['regu'] = FouStaTab.apply(intraweight , axis = 1)\n",
    "FouStaTab.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "FouStaTab = FouStaTab[['carnum','freseq','regu']]\n",
    "FouStaTab.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "FouStaTab.to_csv('FouStaTab.csv',encoding = 'gbk',index=False)\n",
    "len(FouStaTab)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6.5 各阶段频繁出行模式数据读入及处理\n",
    "\n",
    "由于数据量过大，每次提取完一阶段的频繁出行模式都需要关闭jupyter并清除内存，这导致内存中的FirStaTab，SecStaTab，FouStaTab都消失了，因此需要重新读入，并将频繁出行模式从字符串重新转换为list格式。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def str2list(x):\n",
    "    if type(x) == float:\n",
    "        return x\n",
    "    else:\n",
    "        return eval(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "FirStaTab = pd.read_csv(\"FirStaTab.csv\",encoding='gbk')\n",
    "SecStaTab = pd.read_csv(\"SecStaTab.csv\",encoding='gbk')\n",
    "FouStaTab = pd.read_csv(\"FouStaTab.csv\",encoding='gbk')\n",
    "# 频繁出行模式字符串转化为list\n",
    "FirStaTab['freseq'] = FirStaTab[['freseq']].applymap(str2list)\n",
    "SecStaTab['freseq'] = SecStaTab[['freseq']].applymap(str2list)\n",
    "FouStaTab['freseq'] = FouStaTab[['freseq']].applymap(str2list)\n",
    "type(FirStaTab.iloc[0,:]['freseq'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7.各阶段frequent sequence activity pattern及regularity聚合到一张dataframe中去，并进行阶段间similarity计算，此外，挖掘出各个阶段的被检测频次均值"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7.1 第一二三（原四）阶段聚合为一张dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "AllStaPat = pd.merge(FirStaTab[['carnum','freseq','regu']],SecStaTab[['carnum','freseq','regu']],on ='carnum' ,how='left')\n",
    "AllStaPat.rename(columns={'freseq_x':'freseq1','regu_x':'regu1','freseq_y':'freseq2','regu_y':'regu2'},inplace=True)\n",
    "# AllStaPat = pd.merge(AllStaPat,ThiStaTab[['carnum','freseq','regu']],on ='carnum' ,how='left' )\n",
    "# AllStaPat.rename(columns={'freseq':'freseq3','regu':'regu3'},inplace = True)\n",
    "AllStaPat = pd.merge(AllStaPat,FouStaTab[['carnum','freseq','regu']],on ='carnum' ,how='left' )\n",
    "AllStaPat.rename(columns={'freseq':'freseq4','regu':'regu4'},inplace = True)\n",
    "AllStaPat.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# 释放掉FirStaTab，SecStaTab，ThiStaTab，FouStaTab以减少内存开始；\n",
    "# 运行到这里内存只剩下8个G了，只剩下AllStaPat就可以了，其他变量都释放掉。\n",
    "import gc\n",
    "del FirStaTab\n",
    "del SecStaTab\n",
    "# del ThiStaTab\n",
    "del FouStaTab\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7.2 similarity计算\n",
    "\n",
    "similarity计算出第一二阶段的similarity，第一三阶段的similarity，第一四阶段的similarity。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calsimimat(fp1,fp2):\n",
    "    # 计算两个pattern的similarity矩阵，第i行第j列是fp1第i个pattern和fp2第j个pattern的similarity\n",
    "    freseqmat = np.zeros( shape = (len(fp1) , len(fp2)) )\n",
    "    for i in range(len(fp1)):\n",
    "        for j in range(len(fp2)):\n",
    "            freseqmat[i][j] = pattern_simi(fp1[i][0:-3] , fp2[j][0:-3])\n",
    "    return freseqmat\n",
    "    \n",
    "calsimimat([[['09', 120.186, 29.3319], ['20', 120.114, 29.3432], (1, 1), 4, [(2, 1), (6, 1), (7, 1)]], [['07', 120.186, 29.3319], ['17', 120.119, 29.3431], (2, 1), 4, [(1, 1), (6, 1), (7, 1)]], [['07', 120.186, 29.3319], ['17', 120.122, 29.3433], (6, 1), 4, [(1, 1), (2, 1), (7, 1)]], [['07', 120.186, 29.3319], ['17', 120.119, 29.3431], (7, 1), 4, [(1, 1), (2, 1), (6, 1)]]],\n",
    "          [[['07', 120.186, 29.3319], ['18', 120.114, 29.3432], (1, 1), 4, [(2, 1), (6, 1), (7, 1)]], [['07', 120.186, 29.3319], ['17', 120.119, 29.3431], (2, 1), 4, [(1, 1), (6, 1), (7, 1)]], [['07', 120.186, 29.3319], ['17', 120.122, 29.3433], (6, 1), 4, [(1, 1), (2, 1), (7, 1)]], [['07', 120.186, 29.3319], ['17', 120.119, 29.3431], (7, 1), 4, [(1, 1), (2, 1), (6, 1)]]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def inter_simi12(ser):\n",
    "    if ser.name%10000 == 0:\n",
    "        time_end = time.time()\n",
    "        consumedtime = time_end - time_begin\n",
    "        print(\"已经处理到第{0}行,共耗时{1}分钟.\".format(ser.name , round(consumedtime/60 , 3)))\n",
    "    # 计算第一二阶段的inter similarity\n",
    "    if type(ser[3]) == float or type(ser[1]) == float:\n",
    "        return 0\n",
    "    else:\n",
    "        fp1=ser[1] ; fp2=ser[3]\n",
    "        # 判断fp1和fp2的长度，若长度大于70，则仅保留最后70个频繁出行模式\n",
    "        if len(fp1) > 70:\n",
    "            fp1 = fp1[-70:]\n",
    "        if len(fp2) > 70:\n",
    "            fp2 = fp2[-70:]\n",
    "        # 得到两个frequent pattern list间的相似性矩阵\n",
    "        freseqmat = calsimimat(fp1,fp2)\n",
    "        # calculate inter similarity(i,j)i\n",
    "        simi1 = 0 ; weig = 0\n",
    "        for i in range(len(fp1)):\n",
    "            indy = np.argmax(freseqmat[i,:])\n",
    "            tmpweig = np.sqrt(fp1[i][-2] * fp2[indy][-2] / 100)\n",
    "            tmpsimi = freseqmat[i][indy]\n",
    "            simi1 = simi1+(tmpweig*tmpsimi)\n",
    "            weig = weig + tmpweig\n",
    "        simi1 = simi1 / weig\n",
    "        # calculate inter similarity(i,j)j\n",
    "        simi2 = 0 ; weig = 0\n",
    "        for j in range(len(fp2)):\n",
    "            indx = np.argmax(freseqmat[:,j])\n",
    "            tmpweig = np.sqrt(fp2[j][-2] * fp1[indx][-2] / 100)\n",
    "            tmpsimi = freseqmat[indx][j]\n",
    "            simi2 = simi2+(tmpweig*tmpsimi)\n",
    "            weig = weig + tmpweig\n",
    "        simi2 = simi2 / weig\n",
    "        return ((simi1+simi2) / 2)\n",
    "\n",
    "def inter_simi14(ser):\n",
    "    # 计算第一三阶段的inter similarity\n",
    "    if ser.name%10000 == 0:\n",
    "        time_end = time.time()\n",
    "        consumedtime = time_end - time_begin\n",
    "        print(\"已经处理到第{0}行,共耗时{1}分钟.\".format(ser.name , round(consumedtime/60 , 3)))\n",
    "    if type(ser[5]) == float or type(ser[1]) == float:\n",
    "        return 0\n",
    "    else:\n",
    "        fp1=ser[1] ; fp2=ser[5]\n",
    "        # 判断fp1和fp2的长度，若长度大于70，则仅保留最后70个频繁出行模式\n",
    "        if len(fp1) > 70:\n",
    "            fp1 = fp1[-70:]\n",
    "        if len(fp2) > 70:\n",
    "            fp2 = fp2[-70:]\n",
    "        # 得到两个frequent pattern list间的相似性矩阵\n",
    "        freseqmat = calsimimat(fp1,fp2)\n",
    "        # calculate inter similarity(i,j)i\n",
    "        simi1 = 0 ; weig = 0\n",
    "        for i in range(len(fp1)):\n",
    "            indy = np.argmax(freseqmat[i,:])\n",
    "            tmpweig = np.sqrt(fp1[i][-2] * fp2[indy][-2] / 100)\n",
    "            tmpsimi = freseqmat[i][indy]\n",
    "            simi1 = simi1+(tmpweig*tmpsimi)\n",
    "            weig = weig + tmpweig\n",
    "        simi1 = simi1 / weig\n",
    "        # calculate inter similarity(i,j)j\n",
    "        simi2 = 0 ; weig = 0\n",
    "        for j in range(len(fp2)):\n",
    "            indx = np.argmax(freseqmat[:,j])\n",
    "            tmpweig = np.sqrt(fp2[j][-2] * fp1[indx][-2] / 100)\n",
    "            tmpsimi = freseqmat[indx][j]\n",
    "            simi2 = simi2+(tmpweig*tmpsimi)\n",
    "            weig = weig + tmpweig\n",
    "        simi2 = simi2 / weig\n",
    "        return ((simi1+simi2) / 2)\n",
    "\n",
    "# def inter_simi14(ser):\n",
    "#     # 计算第一四阶段的inter similarity\n",
    "#     if type(ser[7]) == float or type(ser[1]) == float:\n",
    "#         return 0\n",
    "#     else:\n",
    "#         fp1=ser[1] ; fp2=ser[7]\n",
    "#         # 得到两个frequent pattern list间的相似性矩阵\n",
    "#         freseqmat = calsimimat(fp1,fp2)\n",
    "#         # calculate inter similarity(i,j)i\n",
    "#         simi1 = 0 ; weig = 0\n",
    "#         for i in range(len(fp1)):\n",
    "#             indy = np.argmax(freseqmat[i,:])\n",
    "#             tmpweig = np.sqrt(fp1[i][-2] * fp2[indy][-2] / 80)\n",
    "#             tmpsimi = freseqmat[i][indy]\n",
    "#             simi1 = simi1+(tmpweig*tmpsimi)\n",
    "#             weig = weig + tmpweig\n",
    "#         simi1 = simi1 / weig\n",
    "#         # calculate inter similarity(i,j)j\n",
    "#         simi2 = 0 ; weig = 0\n",
    "#         for j in range(len(fp2)):\n",
    "#             indx = np.argmax(freseqmat[:,j])\n",
    "#             tmpweig = np.sqrt(fp2[j][-2] * fp1[indx][-2] / 80)\n",
    "#             tmpsimi = freseqmat[indx][j]\n",
    "#             simi2 = simi2+(tmpweig*tmpsimi)\n",
    "#             weig = weig + tmpweig\n",
    "#         simi2 = simi2 / weig\n",
    "#         return ((simi1+simi2) / 2)\n",
    "    \n",
    "time_begin = time.time()\n",
    "AllStaPat.iloc[0:2,:].apply(inter_simi12,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# 计算出第一二、第一三、第一四阶段的similarity\n",
    "time_begin = time.time()\n",
    "AllStaPat['simi12'] = AllStaPat.apply(inter_simi12 , axis=1)\n",
    "# AllStaPat['simi13'] = AllStaPat.apply(inter_simi13 , axis=1)\n",
    "time_begin = time.time()\n",
    "AllStaPat['simi14'] = AllStaPat.apply(inter_simi14 , axis=1)\n",
    "AllStaPat.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "len(AllStaPat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7.3 各阶段日均被检测频次提取"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# 统计第一阶段各天每辆车每次trip的OD的时空信息\n",
    "conn.ping(reconnect=True)\n",
    "cur = conn.cursor()\n",
    "tablenames = ['20191217','20191218','20191220','20200106','20200110','20191223','20191224','20191225','20191226','20191227']\n",
    "FirStaOD = dict()  # 用一个字典来存储第一阶段各天的每辆车每次trip的OD的时空信息\n",
    "for table in tablenames:\n",
    "    sql = '''\n",
    "    select concat(carnum1,carnum2) as carnum , count(*) as freq from {}aj2 where (type = 2 or type = 52) and (lng is not null and lat is not null and \n",
    "    lng > 119 and lng < 121 and lat > 29 and lat < 30)\n",
    "    group by carnum1 , carnum2\n",
    "    '''.format(table)\n",
    "    cur.execute(sql)\n",
    "    conn.commit()\n",
    "    tmptable = cur.fetchall()\n",
    "    tmptable = pd.DataFrame(tmptable)\n",
    "    tmptable.rename(columns = {0:'carnum' , 1:table} , inplace = True)\n",
    "    tmptable = pd.merge(tmptable , AllStaPat[['carnum']] , how = 'inner' , on = ['carnum'])\n",
    "    \n",
    "    # 得到AllStaPat中每一辆车在该日的被检测频次\n",
    "    FirStaOD[table] = tmptable\n",
    "    print(table , \" has query successfully!\")\n",
    "cur.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "FirStaOD['20191217'].head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmptable = FirStaOD['20191217']\n",
    "tablenames = ['20191218','20191220','20200106','20200110','20191223','20191224','20191225','20191226','20191227']\n",
    "for table in tablenames:\n",
    "    tmptable = pd.merge(tmptable , FirStaOD[table] , how = 'outer',on=['carnum'])\n",
    "FirStaOD = tmptable\n",
    "FirStaOD.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 计算第一阶段各车辆的平均每日被检测次数\n",
    "FirStaOD['FirFreq'] = FirStaOD.iloc[:,1:].sum(axis=1) / 10\n",
    "FirStaOD = FirStaOD[['carnum','FirFreq']]\n",
    "FirStaOD.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 统计第二阶段各天每辆车每次trip的OD的时空信息\n",
    "conn.ping(reconnect=True)\n",
    "cur = conn.cursor()\n",
    "tablenames = ['20200210','20200211','20200212','20200213','20200214','20200217','20200218','20200219','20200220','20200221']\n",
    "SecStaOD = dict()  # 用一个字典来存储第一阶段各天的每辆车每次trip的OD的时空信息\n",
    "for table in tablenames:\n",
    "    sql = '''\n",
    "    select concat(carnum1,carnum2) as carnum , count(*) as freq from {}aj2 where (type = 2 or type = 52) and (lng is not null and lat is not null and \n",
    "    lng > 119 and lng < 121 and lat > 29 and lat < 30)\n",
    "    group by carnum1 , carnum2\n",
    "    '''.format(table)\n",
    "    cur.execute(sql)\n",
    "    conn.commit()\n",
    "    tmptable = cur.fetchall()\n",
    "    tmptable = pd.DataFrame(tmptable)\n",
    "    tmptable.rename(columns = {0:'carnum' , 1:table} , inplace = True)\n",
    "    tmptable = pd.merge(tmptable , AllStaPat[['carnum']] , how = 'inner' , on = ['carnum'])\n",
    "    \n",
    "    # 得到AllStaPat中每一辆车在该日的被检测频次\n",
    "    SecStaOD[table] = tmptable\n",
    "    print(table , \" has query successfully!\")\n",
    "cur.close()\n",
    "\n",
    "# 将各天的表联结成一张表\n",
    "tmptable = SecStaOD['20200210']\n",
    "tablenames = ['20200211','20200212','20200213','20200214','20200217','20200218','20200219','20200220','20200221']\n",
    "for table in tablenames:\n",
    "    tmptable = pd.merge(tmptable , SecStaOD[table] , how = 'outer',on=['carnum'])\n",
    "SecStaOD = tmptable\n",
    "SecStaOD.head(5)\n",
    "\n",
    "# 计算第二阶段各车辆的平均每日被检测次数\n",
    "SecStaOD['SecFreq'] = SecStaOD.iloc[:,1:].sum(axis=1) / 10\n",
    "SecStaOD = SecStaOD[['carnum','SecFreq']]\n",
    "SecStaOD.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# 统计第四阶段各天每辆车每次trip的OD的时空信息\n",
    "conn.ping(reconnect=True)\n",
    "cur = conn.cursor()\n",
    "tablenames = ['20200608','20200609','20200610','20200611','20200612','20200615','20200616','20200617','20200618','20200619']\n",
    "FouStaOD = dict()  # 用一个字典来存储第一阶段各天的每辆车每次trip的OD的时空信息\n",
    "for table in tablenames:\n",
    "    sql = '''\n",
    "    select concat(carnum1,carnum2) as carnum , count(*) as freq from {}aj2 where (type = 2 or type = 52) and (lng is not null and lat is not null and \n",
    "    lng > 119 and lng < 121 and lat > 29 and lat < 30)\n",
    "    group by carnum1 , carnum2\n",
    "    '''.format(table)\n",
    "    cur.execute(sql)\n",
    "    conn.commit()\n",
    "    tmptable = cur.fetchall()\n",
    "    tmptable = pd.DataFrame(tmptable)\n",
    "    tmptable.rename(columns = {0:'carnum' , 1:table} , inplace = True)\n",
    "    tmptable = pd.merge(tmptable , AllStaPat[['carnum']] , how = 'inner' , on = ['carnum'])\n",
    "    \n",
    "    # 得到AllStaPat中每一辆车在该日的被检测频次\n",
    "    FouStaOD[table] = tmptable\n",
    "    print(table , \" has query successfully!\")\n",
    "cur.close()\n",
    "\n",
    "# 将各天的表联结成一张表\n",
    "tmptable = FouStaOD['20200608']\n",
    "tablenames = ['20200609','20200610','20200611','20200612','20200615','20200616','20200617','20200618','20200619']\n",
    "for table in tablenames:\n",
    "    tmptable = pd.merge(tmptable , FouStaOD[table] , how = 'outer',on=['carnum'])\n",
    "FouStaOD = tmptable\n",
    "FouStaOD.head(5)\n",
    "\n",
    "# 计算第二阶段各车辆的平均每日被检测次数\n",
    "FouStaOD['FouFreq'] = FouStaOD.iloc[:,1:].sum(axis=1) / 10\n",
    "FouStaOD = FouStaOD[['carnum','FouFreq']]\n",
    "FouStaOD.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 把四个阶段的频次表做联结成一张表\n",
    "AllStaPat = pd.merge(AllStaPat, FirStaOD, how='left',on='carnum')\n",
    "AllStaPat = pd.merge(AllStaPat, SecStaOD, how='left',on='carnum')\n",
    "AllStaPat = pd.merge(AllStaPat, FouStaOD, how='left',on='carnum')\n",
    "AllStaPat.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "del FirStaOD\n",
    "del SecStaOD\n",
    "# del ThiStaTab\n",
    "del FouStaOD\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 8.基于聚类分析疫情对出行行为影响分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# 准备好聚类数据集\n",
    "data = AllStaPat[['regu1','regu2','regu4','simi12','simi14']]\n",
    "data.fillna(0,inplace = True)\n",
    "data.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**使用kmeans聚类算法进行尝试**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import silhouette_score\n",
    "\n",
    "plt.rcParams['figure.dpi'] = 150 #分辨率\n",
    "Scores = [] # 存储不同初始聚类数目的轮廓系数值\n",
    "for k in range(2,18):  \n",
    "    estimator = KMeans(n_clusters=k,init='k-means++', n_init=10, max_iter=300, tol=0.0001, \n",
    "                       precompute_distances='auto', verbose=0, random_state=0, copy_x=True, n_jobs=-1, \n",
    "                       algorithm='auto')  # 构造聚类器  \n",
    "    labels = estimator.fit_predict(data)  \n",
    "    Scores.append(silhouette_score(data , labels , random_state=0))  \n",
    "    print(\"{0} clustering method has been successfully run\".format(k))\n",
    "X = range(2,18)  \n",
    "plt.xlabel('k')  \n",
    "plt.ylabel('Silhouette Coefficient')  \n",
    "plt.plot(X,Scores,'o-')  \n",
    "plt.show()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "estimator = KMeans(n_clusters=3,init='k-means++', n_init=10, max_iter=300, tol=0.0001, \n",
    "                       precompute_distances='auto', verbose=0, random_state=0, copy_x=True, n_jobs=-1, \n",
    "                       algorithm='auto')  # 构造聚类器\n",
    "labels = estimator.fit_predict(data)  \n",
    "print(silhouette_score(data , labels , random_state=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "Scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "AllStaPat['label'] = labels\n",
    "AllStaPat.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8.1对各个簇进行简单分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "clus = []\n",
    "for i in range(3):\n",
    "    clus.append(AllStaPat[AllStaPat['label'] == i])\n",
    "print(len(AllStaPat),len(clus[0]),len(clus[1]),len(clus[2]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# 得到各个簇的描述性统计\n",
    "for i in range(3):\n",
    "    print(\"第{0}个簇.\".format(i))\n",
    "    print(clus[i].describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8.2将各个簇的信息保存到本地"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getLongestPattern(x):\n",
    "    # 对于各个阶段的frequent pattern仅仅保留最长的那一个,且从最长的pattern中找到首次被检测时间最早的。\n",
    "    if type(x) == float:\n",
    "        return x\n",
    "    minind = -1\n",
    "    plen = len(x[-1])-3\n",
    "    if plen == 2:\n",
    "        for ind in range(len(x)):\n",
    "            if int(x[len(x)-1-ind][0][0]) < int(x[minind][0][0]):\n",
    "                minind = len(x)-1-ind\n",
    "        return x[minind]\n",
    "    else:\n",
    "        for ind in range(len(x)):\n",
    "            if len(x[len(x)-1-ind]) -3 < plen:\n",
    "                break\n",
    "            if int(x[len(x)-1-ind][0][0]) < int(x[minind][0][0]):\n",
    "                minind = len(x)-1-ind\n",
    "        return x[minind]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# 把各个簇保存下来，但是由于每辆车都有很多pattern，在此仅保留最长的那一个pattern。\n",
    "for i in range(3):\n",
    "    tmp = AllStaPat[AllStaPat['label'] == i]\n",
    "    tmp['freseq1'] = tmp[['freseq1']].applymap(getLongestPattern)\n",
    "    tmp['freseq2'] = tmp[['freseq2']].applymap(getLongestPattern)\n",
    "#     tmp['freseq3'] = tmp[['freseq3']].applymap(getLongestPattern)\n",
    "    tmp['freseq4'] = tmp[['freseq4']].applymap(getLongestPattern)\n",
    "    tmp.to_csv(\"第{0}个簇出行模式.csv\".format(i) , encoding = 'gbk')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8.3 对各个簇进行更为深入的分析\n",
    "单单从描述性统计结果来对各个簇的行为进行分析是较为粗略的，想要更为细致地了解各个簇的出行行为需要对出行链的特性进行分析，接下来就一个簇一个簇仔细地去进行分析。\n",
    "### 8.3.1 各个簇的出行模式长度均值和中位数计算"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# 得到各个簇各个阶段的出行链长度的均值和中位数\n",
    "def getLen(x):\n",
    "    # 该函数用来返回frequent pattern中的最长的那个pattern的长度。\n",
    "    if type(x) == float:\n",
    "        return 0\n",
    "    return (len(x[-1])-3)\n",
    "clusterLen = []  # 用来存储各个簇的pattern的长度的均值和中位数\n",
    "for i in range(3):\n",
    "    tmp = AllStaPat[AllStaPat['label'] == i]\n",
    "    tmp['freseq1'] = tmp[['freseq1']].applymap(getLen)\n",
    "    tmp['freseq2'] = tmp[['freseq2']].applymap(getLen)\n",
    "#     tmp['freseq3'] = tmp[['freseq3']].applymap(getLen)\n",
    "    tmp['freseq4'] = tmp[['freseq4']].applymap(getLen)\n",
    "    clusterLen.append([np.mean(tmp['freseq1']) , np.mean(tmp['freseq2']), np.mean(tmp['freseq4']),\n",
    "                      np.median(tmp['freseq1']),np.median(tmp['freseq2']), np.median(tmp['freseq4'])])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "clusterLen"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8.3.2 各个簇的首末次出行时间均值计算"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def getLongestPattern(x):\n",
    "    # 对于各个阶段的frequent pattern仅仅保留最长的那一个,且从最长的pattern中找到首次被检测时间最早的。\n",
    "    if type(x) == float:\n",
    "        return x\n",
    "    minind = -1\n",
    "    plen = len(x[-1])-3\n",
    "    if plen == 2:\n",
    "        for ind in range(len(x)):\n",
    "            if int(x[len(x)-1-ind][0][0]) < int(x[minind][0][0]):\n",
    "                minind = len(x)-1-ind\n",
    "        return x[minind]\n",
    "    else:\n",
    "        for ind in range(len(x)):\n",
    "            if len(x[len(x)-1-ind]) -3 < plen:\n",
    "                break\n",
    "            if int(x[len(x)-1-ind][0][0]) < int(x[minind][0][0]):\n",
    "                minind = len(x)-1-ind\n",
    "        return x[minind]\n",
    "\n",
    "def getFirDec(x):\n",
    "    # 获得车辆的最长的frequent pattern的首次被检测时间\n",
    "    if type(x) == float:\n",
    "        return None\n",
    "    pat = getLongestPattern(x)\n",
    "    return int(pat[0][0])\n",
    "\n",
    "def getLastDec(x):\n",
    "    # 获得车辆的最长的frequent pattern的末次被检测时间\n",
    "    if type(x) == float:\n",
    "        return None\n",
    "    pat = getLongestPattern(x)\n",
    "    return int(pat[-4][0])\n",
    "\n",
    "clusterFirLastDec = []  # 用来存储各个簇的pattern的首末次被检测时间均值和末次被检测时间均值\n",
    "for i in range(3):\n",
    "    tmp = AllStaPat[AllStaPat['label'] == i]\n",
    "    tmp['FirDec1'] = tmp[['freseq1']].applymap(getFirDec)\n",
    "    tmp['FirDec2'] = tmp[['freseq2']].applymap(getFirDec)\n",
    "#     tmp['FirDec3'] = tmp[['freseq3']].applymap(getFirDec)\n",
    "    tmp['FirDec4'] = tmp[['freseq4']].applymap(getFirDec)\n",
    "    tmp['LastDec1'] = tmp[['freseq1']].applymap(getLastDec)\n",
    "    tmp['LastDec2'] = tmp[['freseq2']].applymap(getLastDec)\n",
    "#     tmp['LastDec3'] = tmp[['freseq3']].applymap(getLastDec)\n",
    "    tmp['LastDec4'] = tmp[['freseq4']].applymap(getLastDec)\n",
    "    \n",
    "    clusterFirLastDec.append([np.mean(tmp['FirDec1']) , np.mean(tmp['FirDec2']), np.mean(tmp['FirDec4']),\n",
    "                      np.mean(tmp['LastDec1']) , np.mean(tmp['LastDec2']), np.mean(tmp['LastDec4'])])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "clusterFirLastDec"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8.4 针对第0个簇的单独分析\n",
    "\n",
    "第0个簇包含的样本数据量很大，有120184辆车，而且该簇第一二、一三阶段的频繁出行模式相似性非常低。该簇绝大多数车辆都是仅仅第一阶段有频繁出行模式，但是第二三阶段都没有频繁出行模式的，因此需要针对这个簇单独进行深入分析探究。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "tmp = AllStaPat[AllStaPat['label'] == 0]\n",
    "tmp = tmp[(tmp['regu2'].notnull()) | (tmp['regu4'].notnull())]\n",
    "print(len(tmp))\n",
    "tmp.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmp2 = AllStaPat[AllStaPat['label'] == 0]\n",
    "tmp2 = tmp2[(tmp2['SecFreq'].notnull()) | (tmp2['FouFreq'].notnull())]\n",
    "print(len(tmp2))\n",
    "tmp2.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 各个簇的出行模式长度均值和中位数计算\n",
    "clusterLen = []  # 用来存储各个簇的pattern的长度的均值和中位数\n",
    "tmp['freseq1'] = tmp[['freseq1']].applymap(getLen)\n",
    "tmp['freseq2'] = tmp[['freseq2']].applymap(getLen)\n",
    "#     tmp['freseq3'] = tmp[['freseq3']].applymap(getLen)\n",
    "tmp['freseq4'] = tmp[['freseq4']].applymap(getLen)\n",
    "clusterLen.append([np.mean(tmp['freseq1']) , np.mean(tmp['freseq2']), np.mean(tmp['freseq4']),\n",
    "                  np.median(tmp['freseq1']),np.median(tmp['freseq2']), np.median(tmp['freseq4'])])\n",
    "clusterLen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# 计算下首末次被检测时间\n",
    "clusterFirLastDec = []  # 用来存储各个簇的pattern的首末次被检测时间均值和末次被检测时间均值\n",
    "tmp = AllStaPat[AllStaPat['label'] == 1]\n",
    "tmp = tmp[(tmp['regu2'].notnull()) | (tmp['regu4'].notnull())]\n",
    "tmp['FirDec1'] = tmp[['freseq1']].applymap(getFirDec)\n",
    "tmp['FirDec2'] = tmp[['freseq2']].applymap(getFirDec)\n",
    "#     tmp['FirDec3'] = tmp[['freseq3']].applymap(getFirDec)\n",
    "tmp['FirDec4'] = tmp[['freseq4']].applymap(getFirDec)\n",
    "tmp['LastDec1'] = tmp[['freseq1']].applymap(getLastDec)\n",
    "tmp['LastDec2'] = tmp[['freseq2']].applymap(getLastDec)\n",
    "#     tmp['LastDec3'] = tmp[['freseq3']].applymap(getLastDec)\n",
    "tmp['LastDec4'] = tmp[['freseq4']].applymap(getLastDec)\n",
    "\n",
    "clusterFirLastDec.append([np.mean(tmp['FirDec1']) , np.mean(tmp['FirDec2']), np.mean(tmp['FirDec4']),\n",
    "                  np.mean(tmp['LastDec1']) , np.mean(tmp['LastDec2']), np.mean(tmp['LastDec4'])])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "clusterFirLastDec"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8.5 各个簇出行者在各阶段出行行为分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # 将挖掘得到的各阶段出行者频繁出行模式(不包括回归变量)数据保存至本地\n",
    "# AllStaPat.to_csv(\"完整数据(不包括回归变量).csv\",encoding='gbk')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# 读取数据，并将字符串转化为python保存的格式\n",
    "AllStaPat = pd.read_csv(\"完整数据(不包括回归变量).csv\",encoding='gbk')\n",
    "AllStaPat.drop(columns=['Unnamed: 0'],inplace=True)\n",
    "def str2list(x):\n",
    "    if type(x) == float:\n",
    "        return x\n",
    "    else:\n",
    "        return eval(x)\n",
    "\n",
    "# 测试一下正确性\n",
    "print(type(AllStaPat[['freseq1']].iloc[0:3,:].applymap(str2list).iloc[0,0]))\n",
    "AllStaPat['freseq1'] = AllStaPat[['freseq1']].applymap(str2list)\n",
    "AllStaPat['freseq2'] = AllStaPat[['freseq2']].applymap(str2list)\n",
    "# AllStaPat['freseq3'] = AllStaPat[['freseq3']].applymap(str2list)\n",
    "AllStaPat['freseq4'] = AllStaPat[['freseq4']].applymap(str2list)\n",
    "AllStaPat.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8.5.1各阶段出行被检测频次分布分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# 得到第0个簇各个阶段的小时出行量分布\n",
    "conn.ping(reconnect=True)\n",
    "cur = conn.cursor()\n",
    "tablenames = ['20191217','20191218','20191220','20200106','20200110','20191223','20191224','20191225','20191226','20191227',\n",
    "              '20200210','20200211','20200212','20200213','20200214','20200217','20200218','20200219','20200220','20200221',\n",
    "              '20200608','20200609','20200610','20200611','20200612','20200615','20200616','20200617','20200618','20200619']\n",
    "df = pd.DataFrame(columns=['date'])\n",
    "for table in tablenames:\n",
    "    sql = '''\n",
    "    SELECT concat(carnum1 , carnum2) as carnum , DATE_FORMAT(time, '%Y-%m-%d %H') AS date FROM `{}aj2` \n",
    "    '''.format(table)\n",
    "    cur.execute(sql)\n",
    "    conn.commit()\n",
    "    tmptable = cur.fetchall()\n",
    "    tmptable = pd.DataFrame(tmptable)\n",
    "    tmptable.rename(columns={0:'carnum' , 1:'date'} , inplace=True)\n",
    "    tmptable = pd.merge(tmptable , AllStaPat[AllStaPat['label'] == 0][['carnum']] , on='carnum' , how='inner')\n",
    "    tmptable['date'] = tmptable[['date']].applymap(lambda x : x[11:13])\n",
    "    tmptable = tmptable.groupby(['date'])[['carnum']].count().reset_index()\n",
    "    tmptable.rename(columns={'carnum':table},inplace=True)\n",
    "    df = pd.merge(df , tmptable , how = 'outer' , on = 'date')\n",
    "    print(table , '已经处理完毕！')\n",
    "cur.close()\n",
    "df.fillna(0,inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df['firsta'] = df.iloc[:,1:11].mean(axis = 1)\n",
    "df['secsta'] = df.iloc[:,11:21].mean(axis = 1)\n",
    "df['thista'] = df.iloc[:,21:31].mean(axis = 1)\n",
    "df.sort_values(['date'],inplace=True)\n",
    "df.to_excel(\"第0个簇小时被检测频次分布.xlsx\" , encoding='gbk' , index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# 得到第1个簇各个阶段的小时出行量分布\n",
    "conn.ping(reconnect=True)\n",
    "cur = conn.cursor()\n",
    "tablenames = ['20191217','20191218','20191220','20200106','20200110','20191223','20191224','20191225','20191226','20191227',\n",
    "              '20200210','20200211','20200212','20200213','20200214','20200217','20200218','20200219','20200220','20200221',\n",
    "              '20200608','20200609','20200610','20200611','20200612','20200615','20200616','20200617','20200618','20200619']\n",
    "df = pd.DataFrame(columns=['date'])\n",
    "for table in tablenames:\n",
    "    sql = '''\n",
    "    SELECT concat(carnum1 , carnum2) as carnum , DATE_FORMAT(time, '%Y-%m-%d %H') AS date FROM `{}aj2` \n",
    "    '''.format(table)\n",
    "    cur.execute(sql)\n",
    "    conn.commit()\n",
    "    tmptable = cur.fetchall()\n",
    "    tmptable = pd.DataFrame(tmptable)\n",
    "    tmptable.rename(columns={0:'carnum' , 1:'date'} , inplace=True)\n",
    "    tmptable = pd.merge(tmptable , AllStaPat[AllStaPat['label'] == 1][['carnum']] , on='carnum' , how='inner')\n",
    "    tmptable['date'] = tmptable[['date']].applymap(lambda x : x[11:13])\n",
    "    tmptable = tmptable.groupby(['date'])[['carnum']].count().reset_index()\n",
    "    tmptable.rename(columns={'carnum':table},inplace=True)\n",
    "    df = pd.merge(df , tmptable , how = 'outer' , on = 'date')\n",
    "    print(table , '已经处理完毕！')\n",
    "cur.close()\n",
    "df.fillna(0,inplace=True)\n",
    "df['firsta'] = df.iloc[:,1:11].mean(axis = 1)\n",
    "df['secsta'] = df.iloc[:,11:21].mean(axis = 1)\n",
    "df['thista'] = df.iloc[:,21:31].mean(axis = 1)\n",
    "df.sort_values(['date'],inplace=True)\n",
    "df.to_excel(\"第1个簇小时被检测频次分布.xlsx\" , encoding='gbk' , index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# 得到第2个簇各个阶段的小时出行量分布\n",
    "conn.ping(reconnect=True)\n",
    "cur = conn.cursor()\n",
    "tablenames = ['20191217','20191218','20191220','20200106','20200110','20191223','20191224','20191225','20191226','20191227',\n",
    "              '20200210','20200211','20200212','20200213','20200214','20200217','20200218','20200219','20200220','20200221',\n",
    "              '20200608','20200609','20200610','20200611','20200612','20200615','20200616','20200617','20200618','20200619']\n",
    "df = pd.DataFrame(columns=['date'])\n",
    "for table in tablenames:\n",
    "    sql = '''\n",
    "    SELECT concat(carnum1 , carnum2) as carnum , DATE_FORMAT(time, '%Y-%m-%d %H') AS date FROM `{}aj2` \n",
    "    '''.format(table)\n",
    "    cur.execute(sql)\n",
    "    conn.commit()\n",
    "    tmptable = cur.fetchall()\n",
    "    tmptable = pd.DataFrame(tmptable)\n",
    "    tmptable.rename(columns={0:'carnum' , 1:'date'} , inplace=True)\n",
    "    tmptable = pd.merge(tmptable , AllStaPat[AllStaPat['label'] == 2][['carnum']] , on='carnum' , how='inner')\n",
    "    tmptable['date'] = tmptable[['date']].applymap(lambda x : x[11:13])\n",
    "    tmptable = tmptable.groupby(['date'])[['carnum']].count().reset_index()\n",
    "    tmptable.rename(columns={'carnum':table},inplace=True)\n",
    "    df = pd.merge(df , tmptable , how = 'outer' , on = 'date')\n",
    "    print(table , '已经处理完毕！')\n",
    "cur.close()\n",
    "df.fillna(0,inplace=True)\n",
    "df['firsta'] = df.iloc[:,1:11].mean(axis = 1)\n",
    "df['secsta'] = df.iloc[:,11:21].mean(axis = 1)\n",
    "df['thista'] = df.iloc[:,21:31].mean(axis = 1)\n",
    "df.sort_values(['date'],inplace=True)\n",
    "df.to_excel(\"第2个簇小时被检测频次分布.xlsx\" , encoding='gbk' , index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**保存至本地，在本地进行绘图操作。**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8.5.2 各阶段各个簇在运车辆数及平均出行强度分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# 得到第0个簇每天的在运车辆数及平均出行强度\n",
    "conn.ping(reconnect=True)\n",
    "cur = conn.cursor()\n",
    "tablenames = ['20191217','20191218','20191220','20200106','20200110','20191223','20191224','20191225','20191226','20191227',\n",
    "              '20200210','20200211','20200212','20200213','20200214','20200217','20200218','20200219','20200220','20200221',\n",
    "              '20200608','20200609','20200610','20200611','20200612','20200615','20200616','20200617','20200618','20200619']\n",
    "results = []  # 将每天的在运车辆数和平均出行强度存起来，格式[在运车辆数,平均出行强度]\n",
    "for table in tablenames:\n",
    "    sql = '''\n",
    "    SELECT concat(carnum1 , carnum2) as carnum  FROM `{}aj2` \n",
    "    '''.format(table)\n",
    "    cur.execute(sql)\n",
    "    conn.commit()\n",
    "    tmptable = cur.fetchall()\n",
    "    tmptable = pd.DataFrame(tmptable)\n",
    "    tmptable.rename(columns={0:'carnum'} , inplace=True)\n",
    "    tmptable = pd.merge(tmptable , AllStaPat[AllStaPat['label'] == 0][['carnum']] , on='carnum' , how='inner')\n",
    "    results.append([tmptable['carnum'].nunique() , (len(tmptable) / tmptable['carnum'].nunique())])\n",
    "    print(table , '已经处理完毕！')\n",
    "cur.close()\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tablenames = ['20191217','20191218','20191220','20200106','20200110','20191223','20191224','20191225','20191226','20191227',\n",
    "              '20200210','20200211','20200212','20200213','20200214','20200217','20200218','20200219','20200220','20200221',\n",
    "              '20200608','20200609','20200610','20200611','20200612','20200615','20200616','20200617','20200618','20200619']\n",
    "tmptable = pd.DataFrame(results)\n",
    "tmptable['Date'] = tablenames\n",
    "tmptable.rename(columns = {0:'vehnum',1:'ATI'},inplace = True)\n",
    "tmptable.to_excel(\"第0个簇在运车辆数及平均出行强度分析.xlsx\" , encoding='gbk' , index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# 得到第1个簇每天的在运车辆数及平均出行强度\n",
    "conn.ping(reconnect=True)\n",
    "cur = conn.cursor()\n",
    "tablenames = ['20191217','20191218','20191220','20200106','20200110','20191223','20191224','20191225','20191226','20191227',\n",
    "              '20200210','20200211','20200212','20200213','20200214','20200217','20200218','20200219','20200220','20200221',\n",
    "              '20200608','20200609','20200610','20200611','20200612','20200615','20200616','20200617','20200618','20200619']\n",
    "results = []  # 将每天的在运车辆数和平均出行强度存起来，格式[在运车辆数,平均出行强度]\n",
    "for table in tablenames:\n",
    "    sql = '''\n",
    "    SELECT concat(carnum1 , carnum2) as carnum  FROM `{}aj2` \n",
    "    '''.format(table)\n",
    "    cur.execute(sql)\n",
    "    conn.commit()\n",
    "    tmptable = cur.fetchall()\n",
    "    tmptable = pd.DataFrame(tmptable)\n",
    "    tmptable.rename(columns={0:'carnum'} , inplace=True)\n",
    "    tmptable = pd.merge(tmptable , AllStaPat[AllStaPat['label'] == 1][['carnum']] , on='carnum' , how='inner')\n",
    "    results.append([tmptable['carnum'].nunique() , (len(tmptable) / tmptable['carnum'].nunique())])\n",
    "    print(table , '已经处理完毕！')\n",
    "cur.close()\n",
    "tmptable = pd.DataFrame(results)\n",
    "tmptable['Date'] = tablenames\n",
    "tmptable.rename(columns = {0:'vehnum',1:'ATI'},inplace = True)\n",
    "tmptable.to_excel(\"第1个簇在运车辆数及平均出行强度分析.xlsx\" , encoding='gbk' , index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# 得到第2个簇每天的在运车辆数及平均出行强度\n",
    "conn.ping(reconnect=True)\n",
    "cur = conn.cursor()\n",
    "tablenames = ['20191217','20191218','20191220','20200106','20200110','20191223','20191224','20191225','20191226','20191227',\n",
    "              '20200210','20200211','20200212','20200213','20200214','20200217','20200218','20200219','20200220','20200221',\n",
    "              '20200608','20200609','20200610','20200611','20200612','20200615','20200616','20200617','20200618','20200619']\n",
    "results = []  # 将每天的在运车辆数和平均出行强度存起来，格式[在运车辆数,平均出行强度]\n",
    "for table in tablenames:\n",
    "    sql = '''\n",
    "    SELECT concat(carnum1 , carnum2) as carnum  FROM `{}aj2` \n",
    "    '''.format(table)\n",
    "    cur.execute(sql)\n",
    "    conn.commit()\n",
    "    tmptable = cur.fetchall()\n",
    "    tmptable = pd.DataFrame(tmptable)\n",
    "    tmptable.rename(columns={0:'carnum'} , inplace=True)\n",
    "    tmptable = pd.merge(tmptable , AllStaPat[AllStaPat['label'] == 2][['carnum']] , on='carnum' , how='inner')\n",
    "    results.append([tmptable['carnum'].nunique() , (len(tmptable) / tmptable['carnum'].nunique())])\n",
    "    print(table , '已经处理完毕！')\n",
    "cur.close()\n",
    "tmptable = pd.DataFrame(results)\n",
    "tmptable['Date'] = tablenames\n",
    "tmptable.rename(columns = {0:'vehnum',1:'ATI'},inplace = True)\n",
    "tmptable.to_excel(\"第2个簇在运车辆数及平均出行强度分析.xlsx\" , encoding='gbk' , index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**数据保存至本地，在本地绘图**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8.5.3 首末次被检测时间分布分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def getLongestPattern(x):\n",
    "    # 对于各个阶段的frequent pattern仅仅保留最长的那一个,且从最长的pattern中找到首次被检测时间最早的。\n",
    "    if type(x) == float:\n",
    "        return x\n",
    "    minind = -1\n",
    "    plen = len(x[-1])-3\n",
    "    if plen == 2:\n",
    "        for ind in range(len(x)):\n",
    "            if int(x[len(x)-1-ind][0][0]) < int(x[minind][0][0]):\n",
    "                minind = len(x)-1-ind\n",
    "        return x[minind]\n",
    "    else:\n",
    "        for ind in range(len(x)):\n",
    "            if len(x[len(x)-1-ind]) -3 < plen:\n",
    "                break\n",
    "            if int(x[len(x)-1-ind][0][0]) < int(x[minind][0][0]):\n",
    "                minind = len(x)-1-ind\n",
    "        return x[minind]\n",
    "\n",
    "def getFirDec(x):\n",
    "    # 获得车辆的最长的frequent pattern的首次被检测时间\n",
    "    if type(x) == float:\n",
    "        return None\n",
    "    pat = getLongestPattern(x)\n",
    "    return int(pat[0][0])\n",
    "\n",
    "def getLastDec(x):\n",
    "    # 获得车辆的最长的frequent pattern的末次被检测时间\n",
    "    if type(x) == float:\n",
    "        return None\n",
    "    pat = getLongestPattern(x)\n",
    "    return int(pat[-4][0])\n",
    "\n",
    "for i in range(3):\n",
    "    tmp = AllStaPat[AllStaPat['label'] == i]\n",
    "    tmp['ft1'] = tmp[['freseq1']].applymap(getFirDec)\n",
    "    tmp['ft2'] = tmp[['freseq2']].applymap(getFirDec)\n",
    "    tmp['ft4'] = tmp[['freseq4']].applymap(getFirDec)\n",
    "    tmp['lt1'] = tmp[['freseq1']].applymap(getLastDec)\n",
    "    tmp['lt2'] = tmp[['freseq2']].applymap(getLastDec)\n",
    "    tmp['lt4'] = tmp[['freseq4']].applymap(getLastDec)\n",
    "    tmp[['carnum','ft1','lt1','ft2','lt2','ft4','lt4']].to_csv(\"第\"+str(i)+\"个簇各阶段首末次被检测时间.csv\",encoding='gbk',index=False)\n",
    "    print(i,\"个簇已经保存\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**数据保存至本地，在本地绘图**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 9.不同出行群体面对疫情的行为反应\n",
    "\n",
    "疫情对不同出行群体的影响分析主要分析某一出行特征的群体受疫情影响倾向于表现出怎么样的行为，比如以前出行频次很高，出行很规律的人受疫情影响会更小，比如以前出行频次低的人受疫情影响更大等等。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9.1 时间维度出行行为特征提取\n",
    "\n",
    "提取出出行者第一阶段的出行行为特征，将一天划分为五个阶段，分别是Before dawn(00:00-4:00AM),Early morning(4:00–6:00AM)，Morning(6:00-12:00AM)，Afternoon(12:00-17:00PM)，Evening(17:00-21:00PM)，Night(21:00-24:00PM)，分别统计第一阶段各个时间段的每日被检测频次均值和方差。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 统计第一阶段各天每辆车在各个时间段的被检测频次\n",
    "conn.ping(reconnect=True)\n",
    "cur = conn.cursor()\n",
    "tablenames = ['20191217','20191218','20191220','20200106','20200110','20191223','20191224','20191225','20191226','20191227']\n",
    "fretab = dict()  # 用一个字典来存储第一阶段各天的每辆车每次trip的OD的时空信息\n",
    "for table in tablenames:\n",
    "    sql = '''\n",
    "    select carnum , tp , count(*) as freq\n",
    "    from(\n",
    "    select carnum , time , \n",
    "    case \n",
    "        when time >= 0 and time < 4 then 0\n",
    "        when time >= 4 and time < 6 then 1\n",
    "        when time >= 6 and time < 12 then 2\n",
    "        when time >= 12 and time < 17 then 3\n",
    "        when time >= 17 and time < 21 then 4\n",
    "        when time >= 21 and time <= 24 then 5\n",
    "        else 6\n",
    "    END as tp\n",
    "    from(\n",
    "    select concat(carnum1,carnum2) as carnum , CAST(substr(time , 12 , 2) AS SIGNED) as time  from {}aj2 where (type = 2 or type = 52) and (lng is not null and lat is not null and \n",
    "        lng > 119 and lng < 121 and lat > 29 and lat < 30) \n",
    "    ) t1 ) t2\n",
    "    group by carnum , tp\n",
    "    '''.format(table)\n",
    "    cur.execute(sql)\n",
    "    conn.commit()\n",
    "    tmptable = cur.fetchall()\n",
    "    tmptable = pd.DataFrame(tmptable)\n",
    "    tmptable.rename(columns = {0:'carnum',1:'tp',2:table+'freq'} ,inplace = True)\n",
    "    tmptable = pd.merge(tmptable , AllStaPat[['carnum']] , how = 'inner' , on = ['carnum'])\n",
    "    \n",
    "    # 得到AllStaPat中每一辆车在该日各个阶段的被检测频次\n",
    "    fretab[table] = tmptable\n",
    "    print(table , \" has query successfully!\")\n",
    "cur.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fretab2 = fretab['20191217']\n",
    "tablenames = ['20191218','20191220','20200106','20200110','20191223','20191224','20191225','20191226','20191227']\n",
    "for table in tablenames:\n",
    "    fretab2 = pd.merge(fretab2,fretab[table],on=['carnum','tp'],how='outer')\n",
    "fretab2.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calfeat(ser , operation):\n",
    "    if operation == 'avg':\n",
    "        return np.mean(ser)\n",
    "    elif operation == 'std':\n",
    "        return np.std(ser)\n",
    "    elif operation == 'count':\n",
    "        return len(ser.dropna())\n",
    "     \n",
    "fretab2['freqavg'] = fretab2.iloc[:,2:12].apply(calfeat, operation='avg' , axis = 1)\n",
    "fretab2['freqstd'] = fretab2.iloc[:,2:12].apply(calfeat, operation='std' , axis = 1)\n",
    "fretab2['count'] = fretab2.iloc[:,2:12].apply(calfeat, operation='count' , axis = 1)\n",
    "fretab2.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# 将fretab2整理成features的透视表的形式\n",
    "fretab2 = fretab2[['carnum','tp','freqavg','freqstd','count']]\n",
    "fretab2 = pd.pivot_table(fretab2 , index=['carnum'] , columns = ['tp'] , values = ['freqavg','freqstd','count'])\n",
    "fretab2.columns = [col[0]+str(col[1]) for col in fretab2.columns.values]\n",
    "fretab2.reset_index(inplace = True)\n",
    "fretab2.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 进行缺失值填补\n",
    "fretab2.fillna(0 , inplace = True)\n",
    "fretab2.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "AllStaPat = pd.merge(AllStaPat , fretab2 , how='left' , on='carnum')\n",
    "AllStaPat.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9.2 每个电警设备距离市中心距离提取"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "采用首先得到每个电警的1km缓冲区范围内的电警密度，然后对每辆车基于电警信息作表联结来得到每辆车的电警密度。存在一个问题是每个电警设备的devicecode都是不同的，但是一些电警的经纬度是相同的，在求每个电警的电警密度时，不将重复的经纬度删除，因为虽然有的电警经纬度重复了，但是这在计算电警密度时依然需要考虑，因为他们是不同的电警，依然反应了靠近市中心的程度，可是计算完后需要删除，因为不删除的话后续进行表联结时会出现一辆车同时与好多个电警设备联结的情况，而这是不正确的，会使一辆车出现很多次重复信息。\n",
    "\n",
    "现在将电警密度这个指标更换成每个电警距离市中心的距离。原来的代码未删除，均进行了注释。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# 读取cameras表\n",
    "conn.ping(reconnect=True)\n",
    "cur = conn.cursor()\n",
    "sql = '''\n",
    "      select LNG , LAT from cameras\n",
    "      '''\n",
    "cur.execute(sql)\n",
    "conn.commit()\n",
    "cam = cur.fetchall()\n",
    "cam = pd.DataFrame(cam)\n",
    "cam.rename(columns = {0:'lng',1:'lat'} ,inplace = True)\n",
    "cur.close()\n",
    "# cam = cam.drop_duplicates()\n",
    "cam.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "## 原本用来计算电警密度的代码全部注释掉了\n",
    "\n",
    "# from scipy.spatial.distance import pdist\n",
    "# from scipy import spatial\n",
    "# # 得到第一阶段频繁出行模式首末次被检测点的电警密度\n",
    "# # 尝试了基于apply方法对每辆车都进行电警密度计算，但是速度实在太慢，跑完首次被检测点电警密度大约需要10个小时，因此放弃该方法\n",
    "# # 因此决定采用首先得到每个电警的1km缓冲区范围内的电警密度，然后对每辆车基于电警信息作表联结来得到每辆车的电警密度\n",
    "\n",
    "# # 定义通过经纬度计算直线距离的函数\n",
    "# from math import radians, cos, sin, asin, sqrt\n",
    "# def geodistance(lng1,lat1,lng2,lat2):\n",
    "#     if lng1 == None or lng2 == None or lat1 == None or lat2 == None:\n",
    "#         return 999999\n",
    "#     elif (lng1 < 119 or lng1 > 121) or (lng2 < 119 or lng2 > 121) or (lat1 < 29 or lat1 > 30) or (lat2 < 29 or lat2 > 30):\n",
    "#         return 999999\n",
    "#     lng1 = float(lng1) ; lng2 = float(lng2) ; lat1 = float(lat1) ; lat2 = float(lat2)\n",
    "#     lng1,lat1,lng2,lat2 = map(radians,[lng1,lat1,lng2,lat2])\n",
    "#     dlon = lng2-lng1\n",
    "#     dlat = lat2-lat1\n",
    "#     a = sin(dlat/2)**2 + cos(lat1) * cos(lat2) * sin(dlon/2)**2\n",
    "#     distance = 2* asin(sqrt(a))*6371*1000\n",
    "#     distance = round(distance/1000,3)\n",
    "#     return distance\n",
    "\n",
    "# def caldis(u,v):\n",
    "#     # 计算输入矩阵中两个向量的距离\n",
    "#     return geodistance(u[0],u[1],v[0],v[1])\n",
    "    \n",
    "# # 将cam转化为向量的形式\n",
    "# X = cam.values\n",
    "# X_mat = spatial.distance.squareform(pdist(X , metric=caldis))\n",
    "# def sma1(ser):\n",
    "#     return (len(ser[ser<=1]))\n",
    "# cam['devdes'] = np.apply_along_axis(sma1, 1, X_mat)\n",
    "# cam['devdes'] = cam['devdes'] / np.pi/1/1  # 缓冲区范围内电警数量除以面积\n",
    "# cam = cam.drop_duplicates()\n",
    "# cam.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# cam.reset_index(drop=True,inplace=True)\n",
    "# cam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import gc\n",
    "# del X_mat\n",
    "# del X\n",
    "# gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "corelng,corelat = bd2gcj(120.108679,29.313118)  # 义乌市中心经纬度坐标\n",
    "\n",
    "# 定义通过经纬度计算直线距离的函数\n",
    "from math import radians, cos, sin, asin, sqrt\n",
    "def geodistance(lng1,lat1,lng2,lat2):\n",
    "    if lng1 == None or lng2 == None or lat1 == None or lat2 == None:\n",
    "        return 999999\n",
    "    elif (lng1 < 119 or lng1 > 121) or (lng2 < 119 or lng2 > 121) or (lat1 < 29 or lat1 > 30) or (lat2 < 29 or lat2 > 30):\n",
    "        return 999999\n",
    "    lng1 = float(lng1) ; lng2 = float(lng2) ; lat1 = float(lat1) ; lat2 = float(lat2)\n",
    "    lng1,lat1,lng2,lat2 = map(radians,[lng1,lat1,lng2,lat2])\n",
    "    dlon = lng2-lng1\n",
    "    dlat = lat2-lat1\n",
    "    a = sin(dlat/2)**2 + cos(lat1) * cos(lat2) * sin(dlon/2)**2\n",
    "    distance = 2* asin(sqrt(a))*6371*1000\n",
    "    distance = round(distance/1000,3)\n",
    "    return distance\n",
    "\n",
    "def caldis2core(ser):\n",
    "    # 计算电警距离市中心的距离\n",
    "    return geodistance(ser[0],ser[1],corelng,corelat)\n",
    "    \n",
    "# 计算每个电警距离市中心的距离\n",
    "cam['dis'] = cam.apply(caldis2core ,axis=1)\n",
    "cam.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9.3 每个电警设备的各POI类型密度提取"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "POI密度这个指标经过试验效果并不好，这个不好体现在几个方面，1.物理含义不好解释，比如餐饮类别密度高，难道就代表着去餐饮店上班的人？科教文化类密度高，难道就代表着去学校上班的老师？2.主要是餐饮、购物、公司等几类POI数量多，其他类别POI数量少，导致密度很低，如科教文化等，这使得科教文化等类别POI密度更难解释。3.POI对结果的影响个人感觉更多的来自于其空间分布。\n",
    "\n",
    "因此，在本研究中干脆去掉POI密度这些自变量不再考虑。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# # 读取义乌poi\n",
    "# df_poi = pd.read_csv(\"./SrcCode/义乌POI.txt\",header=None,names=['ID','name','type','lng','lat'],dtype={'type':object},sep=';',encoding = 'utf-8')\n",
    "# df_poi.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# # 需要对POI数据做一些预处理，否则效果不佳\n",
    "# # 将风景名胜(110000)，\n",
    "# df_poi = df_poi[(df_poi['type']=='050000')|(df_poi['type']=='060000')|(df_poi['type']=='070000')|(df_poi['type']=='170000')]\n",
    "# len(df_poi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# from scipy.spatial.distance import cdist\n",
    "# # 计算各类型poi密度，计算方式为某类型poi/缓冲区内总poi个数   \n",
    "# for item in ['050000','060000','070000','170000']:\n",
    "#     # 计算每个电警缓冲区范围内各个类型poi的数量\n",
    "#     tmp = df_poi[df_poi['type'] == item]\n",
    "#     X_mat = cdist(cam[['lng','lat']].values , tmp[['lng','lat']].values , metric=caldis)\n",
    "#     cam[item] = np.apply_along_axis(sma1, 1, X_mat)\n",
    "#     print(item,\"已经运行完毕.\")\n",
    "# cam.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import gc\n",
    "# del X_mat\n",
    "# del tmp\n",
    "# gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# def calden(ser):\n",
    "#     # 目前已经计算出各个poi类型在各个电警缓冲区范围内的数量,接下来需要计算出各个poi类型的密度\n",
    "#     ser = list(ser)\n",
    "#     return sum(ser[3:])\n",
    "\n",
    "# cam['totnum'] = cam.apply(calden , axis=1)\n",
    "# cam.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# for item in ['050000','060000','070000','170000']:\n",
    "#     cam[item] = cam[item] / cam['totnum']\n",
    "# cam.drop(columns=['totnum'] , inplace=True)\n",
    "# cam.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# 观察是否存在一些电警周围poi密度均为0，即一些电警缓冲区范围内无poi存在\n",
    "# print(len(cam[cam['devdes'].isnull()]))\n",
    "# cam[cam['devdes'].isnull()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**经检查发现这些电警处于义乌市的边界或已经不再义乌市了（当然也存在几个数据有错误的情况）。该情况出现较少，在此不进行处理，后续与频繁模式表进行表联结，保存到本地后，在本地对该现象进行处理，对于不存在poi密度的车辆采取删除操作，防止对后续结论产生影响。**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**由于POI相关变量效果不佳，且难以解释，因此之前将POI相关变量都删除了，相关代码也都注释掉了，现在在本地重新做了一些POI相关分析，并提取了一些相关的自变量，其结果的csv文件已经传送到服务器上了，后面的分析便把相关变量读取进来。**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "df_poi = pd.read_csv(\"各电警土地利用性质及香农熵.csv\",encoding='gbk')\n",
    "df_poi.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_poi和cam两张表中有许多重复的电警点位，需要去重，否则表联结时会使得重复的电警多次联结。\n",
    "print(len(cam),len(df_poi))\n",
    "cam = cam.drop_duplicates()\n",
    "df_poi = df_poi.drop_duplicates()\n",
    "print(len(cam),len(df_poi))\n",
    "# df_poi是读取的csv文件，csv文件保存时会保留7位小数，这导致电警经纬度和原来的有所出入，后面做表联结时产生问题，因此需要重新保留到三位和四位小数\n",
    "df_poi['lng'] = df_poi[['lng']].applymap(lambda x: round(x,3))  \n",
    "df_poi['lat'] = df_poi[['lat']].applymap(lambda x: round(x,4))\n",
    "# cam['lng'] = cam[['lng']].applymap(lambda x: round(x,3))\n",
    "# cam['lat'] = cam[['lat']].applymap(lambda x: round(x,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "cam = pd.merge(cam,df_poi,how='inner',on=['lng','lat'])\n",
    "cam.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#将label转换成哑变量\n",
    "cam = pd.get_dummies(cam , columns = ['label'])\n",
    "cam.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9.4 车辆模式表与电警密度及POI密度进行表联结\n",
    "\n",
    "1. 首先需要提取出AllStaPat中第一阶段频繁出行模式的首次被检测时间，首次被检测时间的提取方法为遍历第一阶段频繁出行模式中最长的pattern，然后从最长的pattern中提取出最早被检测点,找到了最早被检测点之后，在这个pattern中去找首次出行终点。\n",
    "2. 然后将提取出的首次被检测点、首次出行终点与电警密度和poi密度做表联结。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def getdec(ser,t,co):\n",
    "    # 返回首/末次被检测地点的经度or纬度\n",
    "    if ser.name % 200000 == 0:\n",
    "        time_end = time.time()\n",
    "        consumedtime=time_end-time_begin\n",
    "        print(\"已处理到第{0}行，共耗时{1}分钟.\".format(ser.name , round(consumedtime/60,3)))\n",
    "    x = ser[0]\n",
    "    minind = -1\n",
    "    plen = len(x[-1])-3\n",
    "    if plen == 2:\n",
    "        for ind in range(len(x)):\n",
    "            if int(x[len(x)-1-ind][0][0]) < int(x[minind][0][0]):\n",
    "                minind = len(x)-1-ind\n",
    "        if t == 'first':\n",
    "            tmp = x[minind][0]  # 将最早出现点位所在的pattern赋给tmp\n",
    "            if co == 'lng':\n",
    "                return tmp[1]\n",
    "            elif co == 'lat':\n",
    "                return tmp[2]\n",
    "            else:\n",
    "                print(\"请输入lng或者lat.\")\n",
    "        elif t == 'last':\n",
    "            tmp = x[minind][1]  # 将最晚出现点位所在的pattern赋给tmp\n",
    "            if co == 'lng':\n",
    "                return tmp[1]\n",
    "            elif co == 'lat':\n",
    "                return tmp[2]\n",
    "            else:\n",
    "                print(\"请输入lng或者lat.\")\n",
    "        else:\n",
    "            print(\"请输入first或者last.\")\n",
    "    else:\n",
    "        for ind in range(len(x)):\n",
    "            if len(x[len(x)-1-ind]) -3 < plen:\n",
    "                break\n",
    "            if int(x[len(x)-1-ind][0][0]) < int(x[minind][0][0]):\n",
    "                minind = len(x)-1-ind\n",
    "        if t == 'first':\n",
    "            tmp = x[minind][0]  # 将最早出现点位所在的pattern赋给tmp\n",
    "            if co == 'lng':\n",
    "                return tmp[1]\n",
    "            elif co == 'lat':\n",
    "                return tmp[2]\n",
    "            else:\n",
    "                print(\"请输入lng或者lat.\")\n",
    "        elif t == 'last':\n",
    "            patlen = len(x[minind])-3\n",
    "            for i in range(patlen):\n",
    "                if int(x[minind][i][0]) - int(x[minind][0][0]) >= 1:\n",
    "                    break\n",
    "            \n",
    "            tmp = x[minind][i]  # 将首次出行终点所在的pattern赋给tmp\n",
    "            if co == 'lng':\n",
    "                return tmp[1]\n",
    "            elif co == 'lat':\n",
    "                return tmp[2]\n",
    "            else:\n",
    "                print(\"请输入lng或者lat.\")\n",
    "\n",
    "# 测试一下代码的正确性,已验证其正确性\n",
    "# AllStaPat[['freseq1']].iloc[:10,:].apply(getdec,t='last',co='lat',axis=1)\n",
    "             \n",
    "# 获得第一阶段频繁模式首次被检测点经纬度，首次出行终点经纬度\n",
    "time_begin = time.time()\n",
    "AllStaPat['firlng'] = AllStaPat[['freseq1']].apply(getdec,t='first',co='lng',axis=1)\n",
    "time_begin = time.time()\n",
    "AllStaPat['firlat'] = AllStaPat[['freseq1']].apply(getdec,t='first',co='lat',axis=1)\n",
    "time_begin = time.time()\n",
    "AllStaPat['lastlng'] = AllStaPat[['freseq1']].apply(getdec,t='last',co='lng',axis=1)\n",
    "time_begin = time.time()\n",
    "AllStaPat['lastlat'] = AllStaPat[['freseq1']].apply(getdec,t='last',co='lat',axis=1)\n",
    "AllStaPat.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# 进行表联结\n",
    "AllStaPat = pd.merge(AllStaPat , cam[['lng','lat','dis','shan','label_0','label_1','label_2']] , how='inner',left_on=['firlng','firlat'],right_on=['lng','lat'])\n",
    "AllStaPat.drop(columns=['lng','lat'],inplace = True)\n",
    "AllStaPat.rename(columns={'dis':'firdis','shan':'firshan','label_0':'firlabel0','label_1':'firlabel1','label_2':'firlabel2'},inplace=True)\n",
    "AllStaPat = pd.merge(AllStaPat ,cam[['lng','lat','dis','shan','label_0','label_1','label_2']], how='inner',left_on=['lastlng','lastlat'],right_on=['lng','lat'])\n",
    "AllStaPat.drop(columns=['lng','lat'],inplace = True)\n",
    "AllStaPat.rename(columns={'dis':'lastdis','shan':'lastshan','label_0':'lastlabel0','label_1':'lastlabel1','label_2':'lastlabel2'},inplace=True)\n",
    "AllStaPat.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "len(AllStaPat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "AllStaPat.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9.5数据保存至本地"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "数据含义说明\n",
    "carnum:加密后的车牌\n",
    "freseq1:第一阶段频繁模式（直接读进python里是字符串格式，需要自行转化为list格式，使用eval函数可做到）\n",
    "regu1：第一阶段的regularity\n",
    "freseq2:第二阶段频繁模式\n",
    "regu2：第二阶段的regularity\n",
    "freseq4:第四阶段频繁模式\n",
    "regu4：第四阶段的regularity\n",
    "simi12:第一二阶段频繁出行模式的similarity\n",
    "simi14:第一四阶段频繁出行模式的similarity\n",
    "FirFreq:第一阶段平均每日被检测频次均值\n",
    "SecFreq：第二阶段平均每日被检测频次均值\n",
    "FouFreq：第四阶段平均每日被检测频次均值\n",
    "label:聚类后的簇标签，为0-2，各个簇的特性见我写的word\n",
    "count0-count5:一天划分为6个时间段，各个时间段车辆有出行的天数，该值可以为0-5\n",
    "freqavg0-5:各个时间段被检测频次均值\n",
    "freqstd0-5：各个时间段被检测频次标准差\n",
    "firlng:第一阶段频繁出行模式集合中最长的频繁出行模式中的首次被检测点经度\n",
    "firlat:第一阶段频繁出行模式集合中最长的频繁出行模式中的首次被检测点纬度\n",
    "lastlng:首次出行终点经度（首次出行终点概念详见word）\n",
    "lastlat:首次出行终点纬度（首次出行终点概念详见word）\n",
    "firdis：首次被检测点距离市中心的距离\n",
    "firshan:首次被检测点缓冲区范围内的香农熵\n",
    "firlabel0-firlabel2：首次被检测缓冲区类别\n",
    "lastdis：末次被检测点距离市中心的距离\n",
    "lastshan:末次被检测点缓冲区范围内的香农熵\n",
    "lastlabel0-lastlabel2：末次被检测缓冲区类别\n",
    "'''\n",
    "AllStaPat.to_csv(\"完整数据.csv\" , encoding = 'gbk') # 将提取好的出行特征的数据，包括频繁出行模式保存至本地\n",
    "AllStaPat[['regu1','count0','count1','count2','count3','count4','count5','freqavg0','freqavg1','freqavg2','freqavg3','freqavg4',\n",
    "           'freqavg5','freqstd0','freqstd1','freqstd2','freqstd3','freqstd4','freqstd5','firdis','firshan','firlabel0','firlabel1',\n",
    "           'firlabel2','lastdis','lastshan','lastlabel0','lastlabel1','lastlabel2','label']].to_csv('回归自变量及因变量.csv' , encoding='gbk')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**AllStaPat变量已经被保存至本地，下面的代码实现的功能是从csv文件中读取这些变量，并将字符串格式转化为list格式。若无需从本地的csv读取数据，则下面的代码无需运行。暂时下面的代码先注释掉，需要使用时，则取消注释即可。**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>carnum</th>\n",
       "      <th>freseq1</th>\n",
       "      <th>regu1</th>\n",
       "      <th>freseq2</th>\n",
       "      <th>regu2</th>\n",
       "      <th>freseq4</th>\n",
       "      <th>regu4</th>\n",
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       "      <th>simi14</th>\n",
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       "      <th>firdis</th>\n",
       "      <th>firshan</th>\n",
       "      <th>firlabel0</th>\n",
       "      <th>firlabel1</th>\n",
       "      <th>firlabel2</th>\n",
       "      <th>lastdis</th>\n",
       "      <th>lastshan</th>\n",
       "      <th>lastlabel0</th>\n",
       "      <th>lastlabel1</th>\n",
       "      <th>lastlabel2</th>\n",
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       "      <td>0.950116</td>\n",
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       "      <td>13.774</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>浙G05B820D1068CA24D4CDC7D73E8CE9B26</td>\n",
       "      <td>[[['15', 120.03, 29.2045], ['15', 120.03, 29.2...</td>\n",
       "      <td>0.441640</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[[['16', 120.03, 29.2044], ['16', 120.03, 29.2...</td>\n",
       "      <td>0.373218</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.819756</td>\n",
       "      <td>7.9</td>\n",
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      "text/plain": [
       "                               carnum  \\\n",
       "0  BD6DF8973E1ADE17F8F91BC3BBE22A0786   \n",
       "1  浙G05B820D1068CA24D4CDC7D73E8CE9B26   \n",
       "\n",
       "                                             freseq1     regu1 freseq2  regu2  \\\n",
       "0  [[['08', 120.03, 29.2044], ['20', 120.028, 29....  0.387254     NaN    NaN   \n",
       "1  [[['15', 120.03, 29.2045], ['15', 120.03, 29.2...  0.441640     NaN    NaN   \n",
       "\n",
       "                                             freseq4     regu4  simi12  \\\n",
       "0                                                NaN       NaN     0.0   \n",
       "1  [[['16', 120.03, 29.2044], ['16', 120.03, 29.2...  0.373218     0.0   \n",
       "\n",
       "     simi14  FirFreq  ...  firdis   firshan  firlabel0  firlabel1  firlabel2  \\\n",
       "0  0.000000      2.8  ...  13.378  0.950116          0          1          0   \n",
       "1  0.819756      7.9  ...  13.378  0.950116          0          1          0   \n",
       "\n",
       "   lastdis  lastshan  lastlabel0  lastlabel1  lastlabel2  \n",
       "0   13.774  0.971018           0           1           0  \n",
       "1   13.774  0.971018           0           1           0  \n",
       "\n",
       "[2 rows x 45 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 读取数据，并将字符串转化为python保存的格式\n",
    "AllStaPat = pd.read_csv(\"完整数据.csv\",encoding='gbk')\n",
    "AllStaPat.drop(columns=['Unnamed: 0'],inplace=True)\n",
    "AllStaPat.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def str2list(x):\n",
    "    if type(x) == float:\n",
    "        return x\n",
    "    else:\n",
    "        return eval(x)\n",
    "\n",
    "# 测试一下正确性\n",
    "type(AllStaPat[['freseq1']].iloc[0:3,:].applymap(str2list).iloc[0,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "      <th>freseq1</th>\n",
       "      <th>regu1</th>\n",
       "      <th>freseq2</th>\n",
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       "      <th>freseq4</th>\n",
       "      <th>regu4</th>\n",
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       "      <th>firshan</th>\n",
       "      <th>firlabel0</th>\n",
       "      <th>firlabel1</th>\n",
       "      <th>firlabel2</th>\n",
       "      <th>lastdis</th>\n",
       "      <th>lastshan</th>\n",
       "      <th>lastlabel0</th>\n",
       "      <th>lastlabel1</th>\n",
       "      <th>lastlabel2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BD6DF8973E1ADE17F8F91BC3BBE22A0786</td>\n",
       "      <td>[[[08, 120.03, 29.2044], [20, 120.028, 29.2007...</td>\n",
       "      <td>0.387254</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.8</td>\n",
       "      <td>...</td>\n",
       "      <td>13.378</td>\n",
       "      <td>0.950116</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>13.774</td>\n",
       "      <td>0.971018</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>浙G05B820D1068CA24D4CDC7D73E8CE9B26</td>\n",
       "      <td>[[[15, 120.03, 29.2045], [15, 120.03, 29.2044]...</td>\n",
       "      <td>0.441640</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[[[16, 120.03, 29.2044], [16, 120.03, 29.2044]...</td>\n",
       "      <td>0.373218</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.819756</td>\n",
       "      <td>7.9</td>\n",
       "      <td>...</td>\n",
       "      <td>13.378</td>\n",
       "      <td>0.950116</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>13.774</td>\n",
       "      <td>0.971018</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 45 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                               carnum  \\\n",
       "0  BD6DF8973E1ADE17F8F91BC3BBE22A0786   \n",
       "1  浙G05B820D1068CA24D4CDC7D73E8CE9B26   \n",
       "\n",
       "                                             freseq1     regu1 freseq2  regu2  \\\n",
       "0  [[[08, 120.03, 29.2044], [20, 120.028, 29.2007...  0.387254     NaN    NaN   \n",
       "1  [[[15, 120.03, 29.2045], [15, 120.03, 29.2044]...  0.441640     NaN    NaN   \n",
       "\n",
       "                                             freseq4     regu4  simi12  \\\n",
       "0                                                NaN       NaN     0.0   \n",
       "1  [[[16, 120.03, 29.2044], [16, 120.03, 29.2044]...  0.373218     0.0   \n",
       "\n",
       "     simi14  FirFreq  ...  firdis   firshan  firlabel0  firlabel1  firlabel2  \\\n",
       "0  0.000000      2.8  ...  13.378  0.950116          0          1          0   \n",
       "1  0.819756      7.9  ...  13.378  0.950116          0          1          0   \n",
       "\n",
       "   lastdis  lastshan  lastlabel0  lastlabel1  lastlabel2  \n",
       "0   13.774  0.971018           0           1           0  \n",
       "1   13.774  0.971018           0           1           0  \n",
       "\n",
       "[2 rows x 45 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AllStaPat['freseq1'] = AllStaPat[['freseq1']].applymap(str2list)\n",
    "AllStaPat['freseq2'] = AllStaPat[['freseq2']].applymap(str2list)\n",
    "# AllStaPat['freseq3'] = AllStaPat[['freseq3']].applymap(str2list)\n",
    "AllStaPat['freseq4'] = AllStaPat[['freseq4']].applymap(str2list)\n",
    "AllStaPat.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9.6基于决策树模型的特征选择"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd92f239790>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 4500x3600 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制各个变量的相关性热力图\n",
    "plt.rcParams['figure.dpi'] = 300 #分辨率\n",
    "plt.subplots(figsize=(15, 12))\n",
    "sns.heatmap(AllStaPat[['regu1','count0','count1','count2','count3','count4','count5','freqavg0','freqavg1','freqavg2','freqavg3','freqavg4',\n",
    "           'freqavg5','freqstd0','freqstd1','freqstd2','freqstd3','freqstd4','freqstd5','firdis','firshan','firlabel0','firlabel1',\n",
    "           'firlabel2','lastdis','lastshan','lastlabel0','lastlabel1','lastlabel2']].corr(),\n",
    "           annot=False, vmax=1, square=True,cmap=\"YlGnBu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>regu1</th>\n",
       "      <th>count0</th>\n",
       "      <th>count1</th>\n",
       "      <th>count2</th>\n",
       "      <th>count3</th>\n",
       "      <th>count4</th>\n",
       "      <th>count5</th>\n",
       "      <th>freqavg0</th>\n",
       "      <th>freqavg1</th>\n",
       "      <th>freqavg2</th>\n",
       "      <th>...</th>\n",
       "      <th>firshan</th>\n",
       "      <th>firlabel0</th>\n",
       "      <th>firlabel1</th>\n",
       "      <th>firlabel2</th>\n",
       "      <th>lastdis</th>\n",
       "      <th>lastshan</th>\n",
       "      <th>lastlabel0</th>\n",
       "      <th>lastlabel1</th>\n",
       "      <th>lastlabel2</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>0 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [regu1, count0, count1, count2, count3, count4, count5, freqavg0, freqavg1, freqavg2, freqavg3, freqavg4, freqavg5, freqstd0, freqstd1, freqstd2, freqstd3, freqstd4, freqstd5, firdis, firshan, firlabel0, firlabel1, firlabel2, lastdis, lastshan, lastlabel0, lastlabel1, lastlabel2, label]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 30 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看下存在NULL的行\n",
    "tmp = AllStaPat[['regu1','count0','count1','count2','count3','count4','count5','freqavg0','freqavg1','freqavg2','freqavg3','freqavg4',\n",
    "           'freqavg5','freqstd0','freqstd1','freqstd2','freqstd3','freqstd4','freqstd5','firdis','firshan','firlabel0','firlabel1',\n",
    "           'firlabel2','lastdis','lastshan','lastlabel0','lastlabel1','lastlabel2','label']]\n",
    "tmp[tmp.isnull().T.any()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.38725424, 0.        , 0.        , ..., 1.        , 0.        ,\n",
       "        0.        ],\n",
       "       [0.44163973, 0.        , 0.        , ..., 1.        , 0.        ,\n",
       "        1.        ],\n",
       "       [0.54791821, 0.        , 0.        , ..., 1.        , 0.        ,\n",
       "        0.        ],\n",
       "       ...,\n",
       "       [0.5241342 , 0.        , 0.        , ..., 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       [0.28205175, 0.        , 0.        , ..., 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       [0.61128748, 0.        , 0.        , ..., 0.        , 1.        ,\n",
       "        0.        ]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 离群值检测及处理\n",
    "# 使用马氏距离进行离群值检测\n",
    "# 将自变量转化为numpy.array\n",
    "indepvar = AllStaPat[['regu1','count0','count1','count2','count3','count4','count5','freqavg0','freqavg1','freqavg2','freqavg3','freqavg4',\n",
    "           'freqavg5','freqstd0','freqstd1','freqstd2','freqstd3','freqstd4','freqstd5','firdis','firshan','firlabel0','firlabel1',\n",
    "           'firlabel2','lastdis','lastshan','lastlabel0','lastlabel1','lastlabel2','label']]\n",
    "indepvar = indepvar.dropna()\n",
    "indepvar = indepvar.to_numpy()\n",
    "indepvar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "存在NULL的行共有0行，删除NULL值后还存在245743行数据.\n"
     ]
    }
   ],
   "source": [
    "print(\"存在NULL的行共有{0}行，删除NULL值后还存在{1}行数据.\".format((len(AllStaPat) - len(indepvar)) , len(indepvar)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "已经处理到第0行.\n",
      "已经处理到第100000行.\n",
      "已经处理到第200000行.\n",
      "--- Index of Outliers ----\n",
      "离群值共有33381个数据点,占比为13.58%.\n",
      "(array([    10,     14,     15, ..., 245735, 245736, 245742]),)\n",
      "--- Observations found as outlier -----\n",
      "[[0.746      1.         1.         ... 1.         0.         1.        ]\n",
      " [0.55355156 9.         2.         ... 1.         0.         1.        ]\n",
      " [0.68957688 8.         0.         ... 1.         0.         2.        ]\n",
      " ...\n",
      " [0.42882428 0.         0.         ... 0.         1.         0.        ]\n",
      " [0.63580952 0.         5.         ... 0.         1.         1.        ]\n",
      " [0.61128748 0.         0.         ... 0.         1.         0.        ]]\n"
     ]
    }
   ],
   "source": [
    "# Covariance matrix\n",
    "covariance  = np.cov(indepvar[:,:-1] , rowvar=False)\n",
    "\n",
    "# Covariance matrix power of -1\n",
    "covariance_pm1 = np.linalg.matrix_power(covariance, -1)  #对协方差矩阵取逆\n",
    "\n",
    "# Center point\n",
    "centerpoint = np.mean(indepvar[:,:-1] , axis=0)\n",
    "\n",
    "# Distances between center point and other points\n",
    "distances = []\n",
    "for i, val in enumerate(indepvar[:,:-1]):\n",
    "    if i % 100000 == 0:\n",
    "        print(\"已经处理到第{0}行.\".format(i))\n",
    "    p1 = val\n",
    "    p2 = centerpoint\n",
    "    distance = (p1-p2).T.dot(covariance_pm1).dot(p1-p2)\n",
    "    distances.append(distance)\n",
    "distances = np.array(distances)\n",
    "\n",
    "# Cutoff (threshold) value from Chi-Sqaure Distribution for detecting outliers \n",
    "cutoff = chi2.ppf(0.95, indepvar[:,:-1].shape[1])\n",
    "\n",
    "# Index of outliers\n",
    "outlierIndexes = np.where(distances > cutoff )\n",
    "\n",
    "print('--- Index of Outliers ----')\n",
    "print(\"离群值共有{0}个数据点,占比为{1}%.\".format(len(outlierIndexes[0]) , round(len(outlierIndexes[0]) / len(indepvar)*100 , 2) ))\n",
    "print(outlierIndexes)\n",
    "# array([24, 35, 67, 81])\n",
    "\n",
    "print('--- Observations found as outlier -----')\n",
    "print(indepvar[ distances > cutoff , :])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "212362"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 将上述的离群值删除\n",
    "indepvar = indepvar[ distances <= cutoff , :]\n",
    "len(indepvar)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({0.0: 102452, 1.0: 90038, 2.0: 19872})\n"
     ]
    }
   ],
   "source": [
    "from collections import Counter\n",
    "y_true = indepvar[:,-1]\n",
    "# 查看去除离群值后各个类别的样本数量\n",
    "print(Counter(y_true))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.27173702, 0.        , 0.        , ..., 0.        , 1.        ,\n",
       "        0.        ],\n",
       "       [0.33637548, 0.        , 0.        , ..., 0.        , 1.        ,\n",
       "        0.        ],\n",
       "       [0.46268999, 0.        , 0.        , ..., 0.        , 1.        ,\n",
       "        0.        ],\n",
       "       ...,\n",
       "       [0.53025008, 0.125     , 0.        , ..., 0.        , 1.        ,\n",
       "        0.        ],\n",
       "       [0.43442212, 0.        , 0.        , ..., 1.        , 0.        ,\n",
       "        0.        ],\n",
       "       [0.14670135, 0.        , 0.        , ..., 1.        , 0.        ,\n",
       "        0.        ]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 将马氏距离过滤后的数据进行归一化处理\n",
    "from sklearn import preprocessing\n",
    "min_max_scaler = preprocessing.MinMaxScaler()\n",
    "indepvar_minmax = min_max_scaler.fit_transform(indepvar[:,:-1])\n",
    "indepvar_minmax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({0.0: 102452, 1.0: 90038, 2.0: 19872})\n"
     ]
    }
   ],
   "source": [
    "from collections import Counter\n",
    "# 查看样本类别分布，0和1样本比例3.24比1，属于类别不平衡数据\n",
    "print(Counter(y_true))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({0.0: 102452, 1.0: 102452, 2.0: 102452})\n"
     ]
    }
   ],
   "source": [
    "# 目前数据存在严重的样本不均衡问题，因此采用过采样（SMOTE）的手段，使得样本的各个label的数据量相当,然后做特征选择\n",
    "# 使用imlbearn库中上采样方法中的SMOTE接口\n",
    "from imblearn.over_sampling import BorderlineSMOTE\n",
    "# 定义SMOTE模型，random_state相当于随机数种子的作用\n",
    "smo = BorderlineSMOTE(random_state=0)\n",
    "indepvar_minmax_smo, y_smo = smo.fit_resample(indepvar_minmax , y_true)\n",
    "print(Counter(y_smo))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "RFECV链接：https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.RFECV.html#sklearn.feature_selection.RFECV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ True False False  True  True  True  True  True False  True  True  True\n",
      "  True False False  True  True  True  True  True  True False False False\n",
      "  True  True False False False]\n",
      "[ 1  2  7  1  1  1  1  1  4  1  1  1  1  9 12  1  1  1  1  1  1  3  6 10\n",
      "  1  1  5  8 11]\n"
     ]
    }
   ],
   "source": [
    "# 使用RFECV得到最优的特征数量\n",
    "# Feature ranking with recursive feature elimination and cross-validated selection of the best number of features.\n",
    "from sklearn.feature_selection import RFECV\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import tree\n",
    "\n",
    "# estimator = LogisticRegression(penalty='none' ,random_state=0 ,class_weight='balanced',solver='sag')\n",
    "# estimator = RandomForestClassifier(n_estimators=100 , max_depth= 6 , random_state = 1 , class_weight = 'balanced')\n",
    "estimator = tree.DecisionTreeClassifier(max_depth = 50 , random_state=0,class_weight = 'balanced')\n",
    "selector = RFECV(estimator, step=1, cv=5 ,scoring='accuracy')\n",
    "selector = selector.fit(indepvar_minmax_smo , y_smo)\n",
    "print(selector.support_)\n",
    "print(selector.ranking_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.43616542, 0.32690806, 0.35565961, 0.39613698, 0.40026244,\n",
       "       0.43017879, 0.47338942, 0.49486292, 0.52336739, 0.53236019,\n",
       "       0.53522008, 0.53761144, 0.54144738, 0.55453648, 0.5581707 ,\n",
       "       0.56014887, 0.5606662 , 0.56268014, 0.56129412, 0.56167479,\n",
       "       0.56153489, 0.56150235, 0.56184399, 0.56212379, 0.56232226,\n",
       "       0.5622279 , 0.56241987, 0.56255326, 0.56236455])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 观察最优模型的得分\n",
    "# The cross-validation scores such that grid_scores_[i] corresponds to the CV score of the i-th subset of features.\n",
    "selector.grid_scores_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 3, 4, 5, 6, 7, 9, 10, 11, 12, 15, 16, 17, 18, 19, 20, 24, 25]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selectfeat = []  #筛选出来的特征的index\n",
    "for i in range(len(selector.ranking_)):\n",
    "    if selector.ranking_[i] == 1:\n",
    "        selectfeat.append(i)\n",
    "selectfeat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9.7用筛选出的特征拟合随机森林模型观察效果\n",
    "\n",
    "这一部分仅仅是为了观察一下效果，并不作为分析内容放到论文里的，这一部分对应的分析已经放到本地跑了，详见本地文件夹“筛选出的特征进行随机森林模型拟合”。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(212362, 18)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 得到特征筛选后的数据集\n",
    "indepvar_filt = indepvar[:,:-1][: , selectfeat ]\n",
    "indepvar_filt.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.576     ,  5.        , 10.        , ...,  0.98246006,\n",
       "         6.918     ,  0.98437873],\n",
       "       [ 0.53820702, 10.        , 10.        , ...,  0.9701406 ,\n",
       "        12.663     ,  0.9925649 ],\n",
       "       [ 0.75733333, 10.        ,  4.        , ...,  0.95956639,\n",
       "         3.084     ,  0.96642487],\n",
       "       ...,\n",
       "       [ 0.8537037 ,  7.        ,  1.        , ...,  0.80922886,\n",
       "         2.033     ,  0.96621008],\n",
       "       [ 0.66319866, 10.        , 10.        , ...,  0.9883014 ,\n",
       "         2.771     ,  0.99566563],\n",
       "       [ 0.70177778,  4.        ,  8.        , ...,  0.97377962,\n",
       "         5.219     ,  0.98162391]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 将数据划分为训练集及测试集\n",
    "from sklearn.model_selection import train_test_split\n",
    "# x为数据集的feature，y为label.\n",
    "x_train, x_test, y_train, y_test = train_test_split(indepvar_filt , y_true, test_size = 0.3 ,random_state=0)\n",
    "x_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.34229351, 0.7       , 0.9       , ..., 0.86653259, 0.12878647,\n",
       "        0.91588314],\n",
       "       [0.54581795, 1.        , 0.3       , ..., 0.86092025, 0.35328006,\n",
       "        0.86040006],\n",
       "       [0.35256541, 0.3       , 0.8       , ..., 0.95459392, 0.02722542,\n",
       "        0.95285451],\n",
       "       ...,\n",
       "       [0.55666233, 1.        , 0.1       , ..., 0.90699656, 0.45567738,\n",
       "        0.83798993],\n",
       "       [0.81429314, 0.8       , 0.1       , ..., 0.9674519 , 0.12762498,\n",
       "        0.98780394],\n",
       "       [0.52352926, 0.7       , 0.8       , ..., 0.72793433, 0.34937744,\n",
       "        0.73750562]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 对数据做归一化处理\n",
    "min_max_scaler = preprocessing.MinMaxScaler()\n",
    "x_train_minmax = min_max_scaler.fit_transform(x_train)\n",
    "x_test_minmax = min_max_scaler.transform(x_test)\n",
    "x_test_minmax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({0.0: 71770, 1.0: 63051, 2.0: 13832})\n",
      "Counter({0.0: 71770, 1.0: 71770, 2.0: 71770})\n"
     ]
    }
   ],
   "source": [
    "# 目前数据存在严重的样本不均衡问题，因此采用过采样（SMOTE）的手段，使得样本的各个label的数据量相当,然后做特征选择\n",
    "# 定义SMOTE模型，random_state相当于随机数种子的作用\n",
    "print(Counter(y_train))\n",
    "smo = BorderlineSMOTE(random_state=0)\n",
    "x_train_minmax_smo, y_train_smo = smo.fit_resample(x_train_minmax , y_train)\n",
    "print(Counter(y_train_smo))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[20316  5299  5067]\n",
      " [10867  7843  8277]\n",
      " [ 2038  1317  2685]]\n",
      "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay object at 0x7fd92c745910>\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 900x600 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 训练随机森林模型，并观察随机森林模型效果\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import ConfusionMatrixDisplay, confusion_matrix\n",
    "\n",
    "# clf = LogisticRegression(penalty='none' ,random_state=0 ,class_weight='balanced',solver='sag')\n",
    "clf = RandomForestClassifier(n_estimators=500 , max_depth= 6 , random_state = 1 , class_weight = 'balanced')\n",
    "# clf = GradientBoostingClassifier(n_estimators=100 ,learning_rate=0.1 , max_depth = 12 , random_state = 1)\n",
    "clf.fit(x_train_minmax_smo ,y_train_smo)\n",
    "y_pred = clf.predict(x_test_minmax)\n",
    "# 绘制混淆矩阵\n",
    "cm = confusion_matrix(y_test , y_pred)\n",
    "print(cm)\n",
    "plt.rcParams['figure.dpi'] = 150\n",
    "cm_display = ConfusionMatrixDisplay(cm, display_labels = ['0', '1', '2'])\n",
    "print(cm_display)\n",
    "cm_display.plot(cmap = 'Greens')\n",
    "plt.show()\n",
    "plt.clf()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accurancy: 0.4841388186912367\n",
      "macre-precision: 0.44049336772039177\n",
      "macro-recall: 0.465768340475218\n",
      "macro-f1-score: 0.4192116020996577\n"
     ]
    }
   ],
   "source": [
    "# 计算accurancy，macre-precision，macro-recall\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import recall_score\n",
    "from sklearn.metrics import f1_score\n",
    "\n",
    "print('accurancy:' ,accuracy_score(y_test, y_pred , normalize=True))\n",
    "print('macre-precision:' ,precision_score(y_test, y_pred , average='macro')) # precision\n",
    "print('macro-recall:' , recall_score(y_test, y_pred , average='macro'))  # recall\n",
    "print('macro-f1-score:' , f1_score(y_test, y_pred , average='macro'))  # f1-score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['regu1',\n",
       " 'count2',\n",
       " 'count3',\n",
       " 'count4',\n",
       " 'count5',\n",
       " 'freqavg0',\n",
       " 'freqavg2',\n",
       " 'freqavg3',\n",
       " 'freqavg4',\n",
       " 'freqavg5',\n",
       " 'freqstd2',\n",
       " 'freqstd3',\n",
       " 'freqstd4',\n",
       " 'freqstd5',\n",
       " 'firdis',\n",
       " 'firshan',\n",
       " 'lastdis',\n",
       " 'lastshan']"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_list = ['regu1','count0','count1','count2','count3','count4','count5','freqavg0','freqavg1','freqavg2','freqavg3','freqavg4',\n",
    "           'freqavg5','freqstd0','freqstd1','freqstd2','freqstd3','freqstd4','freqstd5','firdis','firshan','firlabel0','firlabel1',\n",
    "           'firlabel2','lastdis','lastshan','lastlabel0','lastlabel1','lastlabel2']\n",
    "feat_name_list = []\n",
    "for i in range(len(selectfeat)):\n",
    "    feat_name_list.append(feat_list[selectfeat[i]])\n",
    "feat_name_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 特征重要性排序\n",
    "plt.figure(figsize=(4,4))\n",
    "plt.rcParams['figure.dpi'] = 150\n",
    "importance = list(clf.feature_importances_)\n",
    "Impt_Series = pd.Series(importance, index = feat_name_list)\n",
    "Impt_Series.sort_values(ascending = True).plot(kind='barh')\n",
    "plt.xlabel(\"Feature Importance\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 将特征重要性保存到本地去，在本地绘图\n",
    "tmp = pd.DataFrame(list(clf.feature_importances_), columns=['feature importance'])\n",
    "tmp['name'] = feat_name_list\n",
    "tmp.to_excel(\"特征重要性排序.xlsx\",encoding='gbk',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              count2         count4         count3          regu1     label\n",
      "count  119666.000000  119666.000000  119666.000000  119666.000000  119666.0\n",
      "mean        7.584301       5.936983       5.883024       0.623419       0.0\n",
      "std         2.822121       3.062968       3.308084       0.163979       0.0\n",
      "min         0.000000       0.000000       0.000000       0.158620       0.0\n",
      "25%         6.000000       4.000000       3.000000       0.519225       0.0\n",
      "50%         9.000000       7.000000       7.000000       0.631994       0.0\n",
      "75%        10.000000       8.000000       9.000000       0.740625       0.0\n",
      "max        10.000000      10.000000      10.000000       1.000000       0.0\n",
      "              count2         count4         count3          regu1     label\n",
      "count  103133.000000  103133.000000  103133.000000  103133.000000  103133.0\n",
      "mean        8.601515       6.821299       6.234105       0.683062       1.0\n",
      "std         2.342358       3.010999       3.404044       0.137242       0.0\n",
      "min         0.000000       0.000000       0.000000       0.160465       1.0\n",
      "25%         8.000000       5.000000       3.000000       0.599714       1.0\n",
      "50%        10.000000       8.000000       7.000000       0.691238       1.0\n",
      "75%        10.000000       9.000000       9.000000       0.778048       1.0\n",
      "max        10.000000      10.000000      10.000000       1.000000       1.0\n",
      "             count2        count4        count3         regu1    label\n",
      "count  22944.000000  22944.000000  22944.000000  22944.000000  22944.0\n",
      "mean       8.805962      7.398971      5.284562      0.688994      2.0\n",
      "std        2.113752      2.770019      3.380071      0.145602      0.0\n",
      "min        0.000000      0.000000      0.000000      0.190013      2.0\n",
      "25%        9.000000      6.000000      2.000000      0.603990      2.0\n",
      "50%       10.000000      8.000000      5.000000      0.704667      2.0\n",
      "75%       10.000000     10.000000      9.000000      0.791619      2.0\n",
      "max       10.000000     10.000000     10.000000      1.000000      2.0\n"
     ]
    }
   ],
   "source": [
    "# 将最重要的4个特征在各个簇的均值求取出来\n",
    "print(AllStaPat[AllStaPat['label']==0][['count2','count4','count3','regu1','label']].describe())\n",
    "print(AllStaPat[AllStaPat['label']==1][['count2','count4','count3','regu1','label']].describe())\n",
    "print(AllStaPat[AllStaPat['label']==2][['count2','count4','count3','regu1','label']].describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9.8 筛选出的特征的相关性分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>regu1</th>\n",
       "      <th>count2</th>\n",
       "      <th>count3</th>\n",
       "      <th>count4</th>\n",
       "      <th>count5</th>\n",
       "      <th>freqavg0</th>\n",
       "      <th>freqavg2</th>\n",
       "      <th>freqavg3</th>\n",
       "      <th>freqavg4</th>\n",
       "      <th>freqavg5</th>\n",
       "      <th>freqstd2</th>\n",
       "      <th>freqstd3</th>\n",
       "      <th>freqstd4</th>\n",
       "      <th>freqstd5</th>\n",
       "      <th>firdis</th>\n",
       "      <th>firshan</th>\n",
       "      <th>lastdis</th>\n",
       "      <th>lastshan</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.387254</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>2.333333</td>\n",
       "      <td>1.428571</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.745356</td>\n",
       "      <td>1.247219</td>\n",
       "      <td>0.494872</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.378</td>\n",
       "      <td>0.950116</td>\n",
       "      <td>13.774</td>\n",
       "      <td>0.971018</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.441640</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.111111</td>\n",
       "      <td>5.285714</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.067334</td>\n",
       "      <td>5.897076</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.378</td>\n",
       "      <td>0.950116</td>\n",
       "      <td>13.774</td>\n",
       "      <td>0.971018</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      regu1  count2  count3  count4  count5  freqavg0  freqavg2  freqavg3  \\\n",
       "0  0.387254     6.0     3.0     7.0     1.0       0.0  1.666667  2.333333   \n",
       "1  0.441640     9.0     7.0     1.0     0.0       0.0  4.111111  5.285714   \n",
       "\n",
       "   freqavg4  freqavg5  freqstd2  freqstd3  freqstd4  freqstd5  firdis  \\\n",
       "0  1.428571       1.0  0.745356  1.247219  0.494872       0.0  13.378   \n",
       "1  5.000000       0.0  4.067334  5.897076  0.000000       0.0  13.378   \n",
       "\n",
       "    firshan  lastdis  lastshan  label  \n",
       "0  0.950116   13.774  0.971018    0.0  \n",
       "1  0.950116   13.774  0.971018    1.0  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 拼接自变量和因变量\n",
    "tmp = pd.DataFrame(data=indepvar_filt , columns=feat_name_list)\n",
    "tmp['label']=y_true\n",
    "tmp.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd930fde210>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 4500x3600 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制各个变量的相关性热力图\n",
    "plt.rcParams['figure.dpi'] = 300 #分辨率\n",
    "plt.subplots(figsize=(15, 12))\n",
    "sns.heatmap(tmp.iloc[:,:-1].corr(),\n",
    "           annot=False, vmax=1, square=True,cmap=\"YlGnBu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 保存自变量和因变量到本地\n",
    "tmp.to_csv('回归自变量及因变量(经过特征筛选).csv',encoding='gbk')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'conn' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-30-dac12af7ab20>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# 关闭数据库数据库连接\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mconn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"conn closed!\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'conn' is not defined"
     ]
    }
   ],
   "source": [
    "# 关闭数据库数据库连接\n",
    "conn.close()\n",
    "print(\"conn closed!\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
